GB2513472A - Resolving similar entities from a database - Google Patents
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Abstract
A plurality of record sets where each record set includes one or more common attribute values is retrieved from the database. An exemplar record set associated with a first entity record set is selected and a classifier 255 determines a probability that the record set includes records associated with the first entity. The determined probability is then compared to a threshold to assess whether the record set includes records associated with the first entity. The classifier is preferably a random forest classifier and the record sets are preferably financial transaction records processed for a financial institution by a merchant.
Description
Resolving Similar Entities From A Database
Field of the Invention
Embodiments of the present invention generally relate to data analysis and, more specifically, to resolving similar entities from a database.
Description of the Related Art
Obtaining relevant information from large databases can be relatively straightforward in some situations. Particularly, when the data records in a database are well-io structured and it is desired to obtain information in records having a particular value or character string in a particular field, those records can be isolated using filtering functions of database interfacing software. Using combinations of filtering functions, more sophistication can be provided to the way in which records are identified for isolation. The isolated records may then be aggregated so as to provide a report i including all the records that together constitute the desired information.
However, in order to denote database records having commonality, such filtering functions rely on identical attributes across those database records. Tn the real worM, database records may not have identical attributes across those records despite those records being related, or may have identical attributes in a relatively small number of fields (or parts of fields) such that filtering functions are unable to provide isolation of the desired database records from other database records. For example, such problems can occur when a database has database records originating from a number of different sources. The isolation of related database records from other database records is a technical problem that becomes worse as the database becomes larger (e.g., a database having billions of database records), in terms of the number of records present. With the sizes of databases in the real world increasing as time progresses, this problem is expected to worsen over time.
Embodiments of the invention address the problem of identifying related database records that may have not have useful identical attributes whilst excluding unrelated database records, and in particular solve the problem of identifying database records that relate to a common entity but which may have no identical attributes.
3 A first aspect of the invention provides a method for identifying related records from a database storing records for multiple entities as daimed in claim 1.
A second aspect of the invention provides a computer system as daimed in claim 12.
Optional features are listed in the dependent claims or are recited in the detailed
description embodiments.
One advantage of the disclosed technique is that two record sets in a database of records that have no identical attributes, but belong to the same common entity, may be linked to the common entity. Therefore, resolutions that would be missed with jo string comparisons alone are made and incorrect resolutions based only on similar strings are avoided, which improves the resohition precision. Another advantage of the disclosed technique is that it reduces the number of mistaken aggregates resulting from records having similar identifiers despite being associated with different entities. By reducing the number of mistaken aggregates, an aggregated report of record sets thus provided occupies less memory than a corresponding aggregated report produced by filtering functions.
Brief Description of the Drawings
So that the manner in which the above recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
Figure 1 is a block diagram illustrating a computer system configured to implement one or more aspects of the present invention.
Figure 2 is a block diagram of the flow of data in through the appfication server.
Figure 3 illustrates a method for training the classifier, according to one embodiment.
Figure 4 illustrates a method of resolving merchant ID to a merchant, according to one embodiment.
Figure 5 illustrates an example of a computing environment, according to one embodiment.
Detailed Description
Embodiments of the invention may be used to aggregate certain records that are resolved to a common entity, but might not otherwise be grouped with one another.
Assuming a database identifies records from each distinct account of an entity by a distinct ID attribute, then the distinct ID attributes may not be matched correctly to link the records of the accounts with the entity.. In one embodiment, a system jo combines records into ID sets based upon identical IDs, so each TD set contains afl of the records with a particular ID. As this example illustrates, a single entity maybe represented by multiple IDs. To evaluate the full set of records for a single entity each collection of records (the ID sets) associated with the single entity need to be merged together.
For example, embodiments of the invention may be used to aggregate certain financial transaction records that are resolved to a common entity, but might not otherwise be grouped with one another. Assuming a transaction database of a financial institution identifies transaction records from each distinct merchant account of a company by a distinct merchant ID attribute, then the distinct merchant IDs attributes may not be matched correctly to link the transaction records of the accounts with the company. As another example, different franchisees of common franchisor will have spate merchant accounts, making it difficult to aggregate the transaction records associated with all franchisees of the franchisor from the transaction records alone. In one embodiment, a financial analysis system combines transaction records into merchant ID sets based upon identical merchant IDs, so each merchant ID set contains all of the transaction records with a particular merchant ID. As this example illustrates, a single company may be represented by multiple merchant IDs. To evaluate the full set of transaction rccords for a singic cntity (company) cach collcction of financial transaction rccords (the merchant ID sets) associated with the single entity need to be merged together.
Background art for handling transaction records will now be described.
Financial institutions store transactional data for analysis. A financial institution generates transactional data from credit and debit card purchases at companies that have a merchant account with the financial institution. The merchant account may be used to processes individua' credit or debit card purchases. in turn, each such purchase is stored as a transaction record in a transaction database. A transaction record associated with a particular merchant account oftentimes includes a merchant TD attribute that links the transaction record to the merchant account. A merchant TD may be any data type, induding a number, a string, or some combination thereof. The financial institution may then ana'yze the transaction records from one or more merchant accounts. For example, an analysis may involve aggregating the transaction records of a merchant account or particular merchant accounts. The analysis may then compare the performance of the merchant account to that of competing merchant o accounts in the same geographic area.
Although the financial institution stores the transaction records in a database of transactions, certain ana'ysis may require the data to be organized in ways that are not part of the transaction records in the database. These databases contain sets of transaction records that an analysis should group together, even though there is no single attribute vahie that relates the transaction records. For example, if a flnancia institution configures a database of transactions with a merchant TD attribute that Unks each transaction record to a merchant account, then an analysis would easily aggregate transaction records with the same merchant ID together. However, a sing'e company may have multiple merchant accounts with a financial institution. If the financial institution provides distinct merchant IDs for every merchant account, even when multiple merchant accounts belong to a singk company, then it is difficult to aggregate transaction records together from the multip'e merchant accounts of that company.
For instance, a franchise company may have distinct merchant accounts with distinct merchant IDs for each franchisee location, in such a case, an analysis could not aggregate the transaction records of the franchise company together based on identical merchant IDs alone. Instead, an analysis can use similarities between the merchant ID attribute values to aggregate the transaction records of the franchise company together.
Existing techniques rely upon simple tests, such as string comparisons between an attribute in a database of transaction records to detect similarities between groups of transaction records. Transaction records including attribute strings that meet a measure of similarity are then aggregated together for analysis. These techniques may work as tong as the attribute contains strings that are identical or similar for groups of transaction records that should be aggregated together and strings that are distinct for groups of transaction records that should not be aggregated together.
However, such identifiers are not always (or even usually) available. For example, different merchant TDs for the merchant accounts of a single company may prevent an analysis system from aggregating the transaction records of the company together.
Furthermore, transaction records may contain similar identifiers that an analysis system may base aggregations upon, even if the transaction records should not be aggregated together. For example, two different companies may have merchant accounts with similar merchant IDs, which an analysis system could mistakenly match to one company. The analysis system may then mistakenly aggregate the transaction jo records of the two companies together.
As the foregoing illustrates, there remains a need for more effective techniques evaluating financial transaction records.
Tn one embodiment, the analysis system aggregates transaction records from a large collection of merchant TD sets. This aggregation may include ca'culating the average transaction size, the transaction size standard deviation, or the average amount that an individual has spent. The analysis system uses the aggregates to train a classifier. Once trained, the analysis system produces a confidence score of whethertwo merchant ID sets belong to a company, based upon the aggregates from the pair of merchant TD sets.
To associate the merchant 1D sets to the company, the analysis system receives a selection of an exemplar merchant tD set that should be associated with the company and best represents the characteristics of the company. The analysis system compares the exemplar merchant ID set with other merchant ID sets to determine a confidence score. The confidence score represents the likelihood that the exemplary merchant TD set and the other merchant TD set is associated with the company. The analysis system associates every merchant ID set having a confidence scores above a threshold, when compared with the exemplar, to the company. Doing so results in a collection of financial transaction rccords that prcsumably all belong to onc company, dcspitc thc fact that many of such records may include different merchant TDs.
Tn the following description, numerous specific details are set forth to provide a more thorough understanding of the present invention. However, it wifl be apparent that the present invention maybe practiced without one or more of these specific details.
Figure 1 is a block diagram illustrating an example data analysis system 100, according to one embodiment of the present invention. As shown, the data analysis system 100 includes an application server 140 running on a server computing system 130, a client running on a client computer system 110, and at least one transaction database 160.
Further, the client 120, application server 140, and transaction database 160 may communicate over a network 180.
The client 120 represents one or more software applications configured to present data and translate user inputs into requests for data analyses by the application server 140.
jo Tn this embodiment, the client 120 connects to the application server 140. However, several clients 120 may execute on the client computer 110 or several clients 120 on several client computers 110 may interact with the application server 140. Tn one embodiment, the client 120 may be a browser accessing a web service.
Alternatively, the cfient 120 may run on the same server computing system 130 as the application server 140. In any event, a user would interact with the data analysis system 100 through the client 120.
The application serrer 140 is configured to include a merchant resolution tool 150 and an analysis engine 155. The merchant resolution tool 150 links matching merchant IDs to a company. The merchant resolution tool 150 reads data from the transaction database 160. The merchant resolution tool 150 may store resolution data on the sewer computer 130 or on the transaction database 160.
The analysis engine 155 uses the resolution data from the merchant resolution tool 150 to analyze data retrieved from the transaction database i6o. The analysis engine 155 aggregates and compares the transaction records from the transaction database i6o to provide insights about a particular company. For instance, a financial institution may dcsign a data analysis to evaluate thc seasonal spending trends for a franchise company. However, each franchisee of the franchise company may have a distinct merchant account with the financial institution. The financial institution stores the transaction records from the merchant accounts with distinct merchant TDs that associate a transaction record with a merchant account. To evaluate the full set of transaction records for the franchise company the analysis engine 155 needs to merge each collection of financial transaction records from each franchisee together.
Therefore, the analysis engine 155 uses the resolution data from the merchant resolution tool 150 to merge the financial transaction records from each franchisee together into a full set of transaction records for the franchise company in order to evaluate the seasonal spending trends for the franchise company.
In this embodiment, the transaction database 160 stores data records of financial transactions associated with a financial institntion. For example, the transaction database may include data records for a large number of merchant accounts processing credit and debit card transaction. In such a case, each record would include data attributes for the amount spent, the transaction date and time, the address of the o merchant, and a merchant ID to associate the record with a particular merchant account.
The transaction database i6o may be a Relational Database Management System (RDBMS) that stores the transaction data as rows in relational tables. Alternatively, the transaction database 160 maybe stored on the same server computing system 130 as the application server 140. The data records of a financial institution Figure 2 illustrates a flow of data from the transaction database 160 through the merchant resolution tool 150, according to one embodiment of the present invention.
As shown, the transaction database i6o includes merchant ID sets 210. Each merchant ID set 210 includes transaction records 215 with the same merchant ID, such as credit and debit card transactions processed for a single merchant account at a financial institution. The merchant resolution tool 150 includes an aggregator 240, candidate aggregates 242, exemplar aggregates 244, training data set 260, and an identity resolver 250. The identi' resolver 250 itself includes a classifier 255 and a resolve list 270.
In one embodiment, the classifier 255 is a random forest classifier. A random forest classificr is a machinc Icarning algorithm that is gcncrally known to bc highly accuratc on large databases that include discrete, continuous, and missing data, as may be the case for financial transaction records 215 in the transaction database i6o. Random forest classifiers include multiple decision trees. The decision trees evaluate features of input data. In the present context, of financial transaction records that are associated with merchant accounts by a merchant id, the evaluated features may include: Word overlap count and frequency of merchant tD attributes * Word-based cosine similarity weighted by per-term inverse docnment freqnency scores of merchant ID attributes * Character-based cosine similarity of merchant ID attributes * Pthcement of word overlap of merchant ID attributes * Identification of the string ".com" * If the merchant ID attribntes includes a store code * Overlap of prefix or suffix digits in the merchant ID attributes * Whether the provided city is numeric * Matching unique merchant category codes * Fractional difference in average ticket amounts * Standard deviations from the average ticket amounts * Fractional difference in magnitude of the ticket amount variances Note, the dassifier 255 may evaluate a variety of other features, depending on the needs of a particular case and data available from the underlying transaction records. Further one of ordinary skill in the art wifl recognize that a random forest classifier is used as a reference example of a classifier and that a variety of other machine learning dassifiers could be used.
To evaluate the variety of features the dassifier 255 grows decision trees based upon the probability that a selected feature should lead to a certain classification. In the present context, the dassifler 255 grows several decision trees based upon different combinations of the features, so that each decision tree classifies a pair of merchant tD sets 210 as matching the same company or not. The output of the classifier 255 is the percentage of decision trees that classify a pair of merchant ID sets 210 as matching the same company.
To prepare for linking merchant IDs to a company, the classifier 255 grows the decision trccs by training on thc training data sct 260. Thc training data sct 260 includcs pairs of merchant ID sets 210 that match the same company and pairs of merchant TD sets 210 that do not match the same company. The pairs of merchant TD sets 210 that match the same company are classified as positive examples in the training data set 260. The pairs of merchant ID sets 210 that do not match the same company are classified as negative examp'es in the training data set 260. As the classifier 255 processes the features of each pair of merchant ID sets 210 as a positive or negative example, the classifier 255 becomes more accurate by refining the probabilities used in the decision trees.
The training data set 260 may also include difficult edge cases, such as pairs of merchant tD sets 210 that do not match, but have similar merchant ID strings. A pair of merchant ID sets 210 with similar merchant ID strings that should not be linked to the same company is an edge case, because oftentimes similar merchant ID strings come from merchant ID sets 210 that should be linked to the same company. Adding such edge cases to the training data set 260 causes the classifier 255 to adjust the Jo probabilities in the decision trees of the classifier 255 to better classify pairs of merchant ID sets 210 with similar merchant TD attributes.
To create a large training data set 260, the merchant resolution tool 150 may generate pairs of randomly selected merchant ID sets 210, which typically provide negative
training examples.
The training data set 260 may include transaction records 215 retrieved from the transaction database 160, may include synthetic transaction records 215, or may include some combination thereof. While a training data set 260 of 4,000 pairs of merchant ID sets 210 has proven to be effective, the actual size of the training data set 260 may be set as a matter of preference.
Once the classifier 255 is trained, the merchant resolution tool 150 may be used to associate merchant IDs from distinct merchant account to a company, so that the analysis engine 155 may run data analyses on full sets of transaction records 215 from all merchant accounts of the company.
The transaction database 160 is configured to include a mechanism for providing transaction rccords 215 with a common mcrchant TD attributc as merchant ID scts 210.
For example, the transaction database 160 may store transaction records 215 with equal merchant ID attributes together in merchant ID sets 210 or the transaction database may store transaction records 215 sequentially by the value of a transaction date attribute. Regardless of the arrangement of the transaction records 215, the merchant resolution tool 150 may retrieve merchant ID sets 210 from the transaction database 160. -10-
After a user selects a merchant 1D set 210 as an exemplar merchant ID set 210(0), other merchant ID sets 210 maybe considered as candidate merchant ID sets 210(1) throngh 21o(M-1). The user selects the exemplar merchant TD set 210(0) as being representative of the characteristics of the company to be resolved. The exemplar merchant ID set may include a large number of transaction records 215. A large number of transaction records 215 may provide aggregates, such as the average transaction size, that are more accurate than merchant ID sets 210 with fewer transaction records 215. Other factors, such as geographic locations, the merchant ID string, or other business heuristics may also guide the selection of the exemplar jo merchant ID set 210(0) from the available merchant ID sets 210.
When linking merchant IDs to a company, the merchant resolution tool 150 retrieves the transaction records 215 of the exemplar merchant ID set 210(0) and the transaction records 215 of a candidate merchant ID set 210(1). The aggregator 240 aggregates the attributes of the transaction records 215 of the exemp'ar merchant TD set 210(0) to produce exemplar aggregates 244. For example, the aggregator 240 calculates the average transaction size, the transaction size standard deviation, or the average amount that an individual has spent. The merchant ID attribute of the exemplar merchant ID set 210(0) is also included with the exemplar aggregates 244. The aggregator 240 also calculates the candidate aggregates 242 from the candidate merchant ID set 210 and includes the merchant ID attribute of the candidate merchant tD set 210(1) with the candidate aggregates 242. Note that the aggregator 240 may calculate additional aggregate values, according to numerous different designs that the tool developer can choose.
After the aggregator 240 determines the aggregate values, the merchant resolution tool passes the exemplar aggregate 244 and the candidate aggregate 242 to an identity resolver 250. The classifier 255 determines the values used as features in the decision trccs from thc data includcd in thc cxcmplar aggrcgatcs 244 and thc candidatc aggregates 242. The dassifier 255 processes the exemplar aggregate 244 and the candidate aggregate 242 to produce a confidence score between zero and one equal to how likely the exemplar merchant TD set 210(0) matches the candidate merchant TD set 210(1) and should therefore be linked to the same company. If the exemplar merchant ID set 210(0) and the candidate merchant 1D set 201(1) receive a score over some threshold, such as 0.70, then the identity resolver 250 stores the merchant ID of the candidate merchant tD set 201(1) in a resolve list 270.
-11 -The merchant resolution tool 150 compares candidate merchant ID sets 210(2) through 210(M-1) with the exemplar merchant TD set 210(0). The identity resolver 250 adds the merchant ID of each candidate merchant ID set 210(1) through 210(M-1) that produces a high confidence score to the resolve list 270. Therefore, the merchant IDs on the resolve list 270 represent the merchant ID sets 210 that belong to the same company as the exemplar merchant ID set 210(0).
The merchant resolution tool 150 stores the resolve list 270 for use by the analysis jo engine 155. Tn turn, the analysis engine 155 may analyze the full collection of transaction records 215 of the company independent of the various merchant TDs included in the transaction records 215 of the company. For example, if the various merchant IDs in a resolve list 270 associate transaction records 215 with multiple merchant accounts from multiple franchisees of a franchise company. Then the analysis engine 155 shoffid merge the transaction records 215 with the merchant TDs in the resolve list 270 to ana'yze the fufi collection of transaction records 215 of the franchise company.
Figure 3 is a flow diagram of method steps for training the classifier 255, according to one embodiment of the present invention. Although the method steps are described in conjunction with the systems of Figures 1-2 and j, persons of ordinaiy skill in the art will understand that any system configuration to perform the method steps, in any order, is within the scope of the invention.
As shown, method 300 beings at step 305, where a merchant resolution tool 150 creates a training data set 260 of positive examples of pairs of merchant ID sets 210 that link to the same company. The merchant resolution tool 150 adds edge cases to the training data set 210. The edge cases include pairs of merchant ID sets 210 that do not match, but have similar merchant ID strings. Thc cdgc cascs may also include pairs of merchant ID sets 210 that have similar aggregate values, but are from different companies, so are actually negative training examples.
In step 310, the merchant resolution tool io adds randomly selected pairs of merchant ID sets 210 to the training data set 260. The randomly selected pairs of merchant ID sets 210 should include a majority of negative training examples.
-12 -In step 315, the merchant resolution tool 150 submits each merchant iD sets 210 in the training data set 260 to the aggregator 240 to generate candidate aggregates 242.
When training the classifier 255, there is no exemplar merchant ID set 210(0), so all merchant ID sets 210 in the training data set 260 are considered candidates merchant ID sets 210(1) through 210(M-1). A user may review these candidate aggregates 242.
In step 320, the user selects pairs of merchant ID sets 210 that should be linked to the same company as positive training examples.
jo In step 325, the user selects pairs of merchant ID sets 210 that link to different companies as negative training examples. These negative training examples include several difficult edge cases. Additionally, the training data set 210 includes a majority of random selections, so the majority of the pairs of merchant ID sets 210 in the training data set 260 are negative training examples.
In step 330, the merchant resolution tool io trains the dassifier 255 with the training data set 260. As described, the classifier 255 is a random forest learning a'gorithm.
After training the classifier 255 with the training data set 260, the classifier 255 may evaluate a pair of merchant ID sets 210 to produce a confidence score, e.g., a value between zero and one. The confidence score equals the percent of decision trees in the random forest algorithm used by the classifier 255 that determine that both merchant ID sets 210 in the pair should be linked to the same company. Therefore, the classifier 255 is able to produce a confidence score that represents whether a pair of merchant ID sets 210 including an exemplar merchant ID set 210(0) and a candidate merchant ID set 210(1) should be linked to the same company.
Figure 4 is a flow diagram of method steps for linking merchant IDs to a company according to onc cmbodimcnt of thc prcscnt invcntion. Although thc mcthod stcps arc described in conj unction with the systems of Figures 1-2 and, persons of ordinary skifl in the art will understand that any system configuration to perform the method steps, in any order, is within the scope of the invention.
As shown, method 400 beings at step 410, where the merchant resolution tool io receives an exemplar merchant ID as the merchant ID attribute for an exemplar merchant tD set 210(0). As described, a user selects the exemplar merchant tD set -13 - 210(0) as being representative of the characteristics of the financial transaction records 215 associated with a company, e.g., the franchisee that best represents a given franchise company. Alternatively, the system may automatically choose an exemplar merchant ID set 210(0) based on user-specified criteria.
In one embodiment, the merchant resolution tool 150 presents an exemplar selection tool to the user. The exemplar selection toot provides assistance in se'ecting an exemplar merchant ID that is representative of a company to be resolved. The exemplar selection tool may accept a search string from the user to identi' merchant Jo TDs that should potentially be linked to the company. The exemplar selection tool may also use some subset of the company name as the search string. Furthermore, the exemplar selection tool may submit the merchant ID sets 210 associated with the identified merchant IDs to the aggregator 240. The aggregator 240 then computes aggregates 242 that assist the user in sekcting the exemplar merchant ID.
Tn step 420, the merchant resolution tool 150 generates exemplar aggregates 244 for the selected exemplar merchant TD set 210(0). After the merchant resolution tool io retrieves the exemplar merchant ID set 210(0) from the transaction database ibo, the aggregator 240 calculates the average transaction size, the transaction size standard deviation, and the average amount that an individual has spent.
In step 430, the merchant resolution tool 150 generates candidate aggregates 242 for a candidate merchant ID set 210(1). The merchant resolution tool 150 identifies a merchant ID set 210(1) through 210(M-1) that has not been compared to the exemplar merchant ID set 210(0), as the candidate merchant ID set 210(1). Once identified, the merchant resolution tool 150 retrieves the candidate merchant ID set 210(1) from the transaction database i6o, and submits the candidate merchant ID set 210(1) to the aggregator 240. The aggregator 240 generates the candidate aggregates 242.
The aggregation and comparison of every possible merchant ID record set 210(1) through 210(M-1) maybe very time consuming, so reducing the number of comparisons is desirable. Tn one embodiment, the merchant resolution tool 242 does not compare every merchant ID record set 210. The merchant resolution tool 242 skips merchant tD record sets 210 that do not meet a certain qualification. Assuming a franchise company only has franchisee locations in the state of California and the transaction records 215 include an attribute for the address at which the transaction occurred, then the merchant resolution todl 242 would skip those merchant ID record sets 210 that do not include transaction records 215 from California. In this case, the merchant resolution tool 242 reduces the number of comparisons by skipping those merchant ID sets 210 that are not from California.
In step 440, the merchant resolution todl 150 determines if the exemplar merchant ID set 210(0) and the candidate merchant ID set 210(1) match one another and therefore should be linked to the same company. The identity resolver 250 submits the exemplar aggregates 244 and the candidate aggregates 242 to the classifier 255. As described, the o classifier 255 produces a confidence score between zero and one equal to the percent of decision trees in the random forest algorithm used by the classifier 255 that determine that both merchant ID sets 210 in the pair should be linked to the same company. If the classifier 255 produces a confidence score under a threshold, then the method 400 proceeds to step 460. If, however, the confidence score is over the thresho'd, then method 400 proceeds to step 450. While a threshold confidence score of 0.70 has proven to be effective, the actual threshold maybe set as a matter of preference.
In step 450, the identity resolver 250 stores the merchant ID attribute of the candidate merchant ID set 201(1) in a resolve list 270.
In one embodiment, the merchant resolution tool 242 merges the exemplar merchant ID set 240(0) and the candidate merchant ID set 240(1) into a combined merchant ID set, which becomes a new larger exemplar merchant ID set 240(0). Then the merchant resolution tool 242 re-generates the exemplar aggregates 244 for the remaining comparisons. In doing so, the new exemplar merchant ID set 240(0) may better represent the company and improve the resolution of the remaining candidate merchant ID sets 240(2) through 240(M-1).
Tn stcp 460, thc mcrchant rcsolution tool 150 dctcrmincs if thcrc arc morc merchant ID sets 210 in the transaction database 160 that have not been compared. If the merchant resolution tool io determines there is another candidate merchant TD set 210(2) to compare, then the method 400 returns to step 430. Once no more candidate merchant ID sets 210 remain to compare, the merchant resolution tool io links merchant ID sets 210 listed in the res6lve list 270 for the company.
-15 -In step 470, the merchant resolution tool 150 links the exemplar merchant ID set 210(0) with the candidate merchant ID sets 210(1) through 210(M-1) listed in the resolve list 270. As described, the resolution of the merchant TD sets may involve storing a list of merchant ID attributes that the analysis engine i can use to identi1z the transaction records 215 of the company. Alternatively, the merchant resolution tool may link the transaction records 215 of the merchant ID sets 210 on the resolve list 270 to the company by populating an attribute of the transaction records 215 with the company name, so that the analysis engine 155 can query the transaction database i6o for the transaction records 215 belonging to the company.
Figure illustrates an example server computing system io running a merchant resolution tool 150, according to one embodiment. As shown, the server computing system 130 includes, a central processing unit (CPU) 550, a network interface 570, a memory 520, and a storage 530, each connected to an interconnect (bus) 540. The server computing system 130 may also include an T/O device interface 560 connecting T/O devices s8o (e.g., keyboard, display and mouse devices) to the computing system 130. Further, in context of this disclosure, the computing elements shown in server computing system 130 may correspond to a physical computing system (e.g., a system in a data centre) or may be a virtual computing instance executing within a computing cloud.
The CPU 550 retrieves and executes programming instructions stored in memory 520 as well as stores and retrieves application data residing in memory 520. The bus 540 is used to transmit programming instructions and application data between the CPU 550, I/O device interface 6o, storage 530, network interface 570, and memory 520. Note that the CPU 550 is included to be representative of a single CPU, multiple CPUs, a single CPU having multiple processing cores, a CPU with an associate memory management unit, and the like. The memory 520 is generally included to be rcprcscntativc of a random acccss mcmory. Thc storagcs3o may bc a disk drivc storagc device. Although shown as a sing'e unit, the storage 530 maybe a combination of fixed and/or removable storage devices, such as fixed disc drives, removable memory cards, or optica' storage, network attached storage NAS), or a storage area-network (SAN).
The communications between the client 120 and the merchant resolution tool io are transmitted over the network 180 via the network interface 570.
Illustratively, the memory o includes a merchant resolution tool 150, exemplar aggregates 244, candidate aggregates 242, and a resolve list 270. The merchant resolution tool io itself includes an aggregator 240 and a classifier 225. The storage 530 includes a training data set s33, which the merchant resolution tool io uses to train the dassifler 225.
The aggregator 240 generates the exemplar aggregates 244 and the candidate aggregates 242 from transaction records 215 retrieved from the transaction database iôo. The merchant resolution tool 150 issues database queries over the network 180 to jo the transaction database 160 via the network interface 570. Once the aggregator 240 generates the exemplar aggregates 244 and candidate aggregates 242, the merchant resolution tool 150 uses the classifier 225 to determine if the merchant IDs sets 240 should be linked to a company.
Although shown in memrny 520, the merchant resolution toot 150, exemplar aggregates 244, candidate aggregates 242, and resolve list 270, may be stored in memory 520, storage 530, or split between memory 520 and storage 530. Likewise, the training data set 533 maybe stored in memory 520, storage 530, or split between memory 520 and storage 530.
In some embodiments, the database repository 160 may be ocated in the storage 530.
In such a case, the database queries and subsequent responses are transmitted over the bus 540. As described, the client 120 may also be located on the server computing system 130, in which case the client 120 would also be stored in memory 520 and the user would utilize the I/O devices 8o to interact with the client 120 through the I/O device interface 6o.
While the foregoing is directed to embodiments of the present invention, other and furthcr cmbodimcnts of the invention may bc dcviscd without departing from thc basic scope thereof. For examp'e, aspects of the present invention maybe imp'emented in hardware or software or in a combination of hardware and software. One embodiment of the invention may be implemented as a program product for use with a computer system. The program(s) of the program product define functions of the embodiments (induding the methods described herein) and can be contained on a variety of computer-readable storage media. Examples of computer-readable storage media include (i) non-writable storage media (e.g., read-only memory devices within a -17-computer, CD-ROM disks readable by a CD-ROM drive, flash memory, ROM chips or any type of solid-state non-v&atile semiconductor memoiy); and (ii) writable storage media (e.g., floppy disks within a diskette drive or hard-disk drive or any type of soUd-state random-access semiconductor memory) on which alterable information is stored.
The invention has been described above with reference to specific embodiments. It will be understood however, that various modifications and changes may be made thereto without departing from the scope of the invention as set forth in the appended claims.
The foregoing description and drawings are, accordingly, to be regarded in an o illustrative rather than a restrictive sense.
Therefore, the scope of the present invention is determined by the claims that follow.
Claims (20)
- -i8 -Claims 1. A method for identifying related records from a database storing records for multiple entities, the method comprising: retrieving a plurality of record sets, wherein each record set includes one or more of the records sharing a common attribute value; receiving a selection of or selecting an exemplar record set, wherein the exemplar record set comprises a plurality of the records associated with a first entity; for each of the plurality of record sets: determining a probability that the record set stores records associated with the first entity by passing a record set and the exemplar record set to a classifier, wherein the classifier is configured to determine the probability that the record set stores records associated with the first entity, and upon determining the probability exceeds a threshold, resolving the record set as storing records associated with the first entity.
- 2. The method of daim 1, wherein the classifier is a random forest classifier.
- 3. The method of claim 1 or claim 2, further comprising, training the classifier using a plurality of training examples, the training examples comprising: one or more first pairs of record sets, wherein each first pair represent a common entity; and one or more second pairs of record sets, wherein each second pair represents unrelated entities.
- 4. The method of any preceding claim, wherein the classifier evaluates features of each record, including at least one of a word overlap count, word frequency, a word-based or character based cosine similarity, merchant category codes, and numeric city codcs associatcd with cach rccord.
- 5. The method of any preceding claim, wherein the classifier evaluates features of each record including a at least one of a fractional difference in size of an average ticket-size in the record, a standard deviation between the average ticket-sizes in the records, and a fractional difference in a magnitude of ticket-size variances
- 6. The method of any preceding claim, wherein resolving the record set to the exemplar record set comprises: merging the records of the record set into the exemplar record set.
- 7. The method of any preceding claim, further comprising, performing an analysis on a set of the records, wherein the set includes the records of the exemplar record set and the records resolved as associated with the first entity.
- 8. The method of claim 10, further comprising determining, for each transaction jo record set, aggregate values for the attributes of the transaction record set; and determining aggregate values for attributes of the exemplar record set.
- 9. The method of any preceding claim, wherein the records are financial transaction records, e.g. comprising credit or debit transactions processed by a financial institution for a merchant.
- 10. The method of daim 9, wherein attributes of the financial transaction records include one or more of the following: an identification of the merchant from which the financial transaction originates; an identification of the credit or debit account owner; an amonnt of the financial transaction; a date of the financial transaction; a time of the financial transaction; and a location of where the financial transaction originated.
- ii. A computer program comprising machine readable instructions that when executed by computing apparatus causes it to perform the method of any preceding claim.
- 12. A computer system, comprising: a memory; and a processor storing one or more programs configured to perform an operation for identifying related transaction records from a database storing records for multiple entities, the method comprising: retrieving a plurality of record sets, wherein each record set includes one or more of the records sharing a common attribute value; receiving a selection of or selecting an exemplar record set, wherein the exemplar record set comprises a plurality of the records associated with a first entity; for each of the plurality of record sets: determining a probability that the record set stores records associated with the first entity by passing a record set and the exemplar record set to a classifier, wherein the classifier is configured to determine the probability that the record set stores records associated with the first entity, and Jo upon determining the probability exceeds a threshold, resoFving the record set as storing records associated with the first entity.
- 13. The system of claim 12, wherein the classifier is a random forest classifier.
- 14. The system of claim 12 or daim 13, further comprising, training the classifier using a p'urality of training examp'es, the training examples comprising: one or more first pairs of record sets, wherein each first pair represent a common entity; and one or more second pairs of record sets, wherein each second pair represents unrelated entities.
- 15. The system of any of claims 12 to 14, wherein resolving the record set to the exemplar record set comprises: merging the records of the record set into the exemplar record set.
- i6. The system of any of claims 12 to 15, further comprising, performing an analysis on a set of the records, wherein the set includes the records of the exemplar record set and the records resolved as associated with the first entity.
- 17. The system of any of claims 12 to 16, wherein determining a probability comprises passing a transaction record set and the exemplar record set to a classifier, wherein the classifier is configured to determine the probability that the transaction record set stores transaction records associated with the first entity.
- 18. The system of any of claims 12 to 17, further comprising determining, for each transaction record set, aggregate values for the attributes of the transaction record set; and determining aggregate values for attributes of the exemplar record set.
- 19. The system of any of claims 12 to 18, wherein the transaction records are financial transaction records, e.g. comprising credit or debit transactions processed by a financial institution for a merchant.o
- 20. The system of claim 19, wherein attributes of the financial transaction records include one or more of the following: an identification of the merchant from which the financial transaction originates; an identification of the credit or debit account owner; an amount of the financial transaction; a date of the financial transaction; a time of the financial transaction; and a ocation of where the financial transaction originated.
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Cited By (37)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9286373B2 (en) | 2013-03-15 | 2016-03-15 | Palantir Technologies Inc. | Computer-implemented systems and methods for comparing and associating objects |
US9348499B2 (en) | 2008-09-15 | 2016-05-24 | Palantir Technologies, Inc. | Sharing objects that rely on local resources with outside servers |
US9392008B1 (en) | 2015-07-23 | 2016-07-12 | Palantir Technologies Inc. | Systems and methods for identifying information related to payment card breaches |
US9483546B2 (en) | 2014-12-15 | 2016-11-01 | Palantir Technologies Inc. | System and method for associating related records to common entities across multiple lists |
US9495353B2 (en) | 2013-03-15 | 2016-11-15 | Palantir Technologies Inc. | Method and system for generating a parser and parsing complex data |
US9501552B2 (en) | 2007-10-18 | 2016-11-22 | Palantir Technologies, Inc. | Resolving database entity information |
US9514414B1 (en) | 2015-12-11 | 2016-12-06 | Palantir Technologies Inc. | Systems and methods for identifying and categorizing electronic documents through machine learning |
US9715518B2 (en) | 2012-01-23 | 2017-07-25 | Palantir Technologies, Inc. | Cross-ACL multi-master replication |
US9760556B1 (en) | 2015-12-11 | 2017-09-12 | Palantir Technologies Inc. | Systems and methods for annotating and linking electronic documents |
US9880987B2 (en) | 2011-08-25 | 2018-01-30 | Palantir Technologies, Inc. | System and method for parameterizing documents for automatic workflow generation |
US9898335B1 (en) | 2012-10-22 | 2018-02-20 | Palantir Technologies Inc. | System and method for batch evaluation programs |
US9984428B2 (en) | 2015-09-04 | 2018-05-29 | Palantir Technologies Inc. | Systems and methods for structuring data from unstructured electronic data files |
US9996229B2 (en) | 2013-10-03 | 2018-06-12 | Palantir Technologies Inc. | Systems and methods for analyzing performance of an entity |
US10061828B2 (en) | 2006-11-20 | 2018-08-28 | Palantir Technologies, Inc. | Cross-ontology multi-master replication |
US10103953B1 (en) | 2015-05-12 | 2018-10-16 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US10127289B2 (en) | 2015-08-19 | 2018-11-13 | Palantir Technologies Inc. | Systems and methods for automatic clustering and canonical designation of related data in various data structures |
US10133588B1 (en) | 2016-10-20 | 2018-11-20 | Palantir Technologies Inc. | Transforming instructions for collaborative updates |
US10140664B2 (en) | 2013-03-14 | 2018-11-27 | Palantir Technologies Inc. | Resolving similar entities from a transaction database |
US10146853B2 (en) | 2015-05-15 | 2018-12-04 | International Business Machines Corporation | Determining entity relationship when entities contain other entities |
US10180977B2 (en) | 2014-03-18 | 2019-01-15 | Palantir Technologies Inc. | Determining and extracting changed data from a data source |
US10198515B1 (en) | 2013-12-10 | 2019-02-05 | Palantir Technologies Inc. | System and method for aggregating data from a plurality of data sources |
US10235533B1 (en) | 2017-12-01 | 2019-03-19 | Palantir Technologies Inc. | Multi-user access controls in electronic simultaneously editable document editor |
US10452678B2 (en) | 2013-03-15 | 2019-10-22 | Palantir Technologies Inc. | Filter chains for exploring large data sets |
US10579647B1 (en) | 2013-12-16 | 2020-03-03 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US10628834B1 (en) | 2015-06-16 | 2020-04-21 | Palantir Technologies Inc. | Fraud lead detection system for efficiently processing database-stored data and automatically generating natural language explanatory information of system results for display in interactive user interfaces |
US10636097B2 (en) | 2015-07-21 | 2020-04-28 | Palantir Technologies Inc. | Systems and models for data analytics |
US10762102B2 (en) | 2013-06-20 | 2020-09-01 | Palantir Technologies Inc. | System and method for incremental replication |
US10795909B1 (en) | 2018-06-14 | 2020-10-06 | Palantir Technologies Inc. | Minimized and collapsed resource dependency path |
US10838987B1 (en) | 2017-12-20 | 2020-11-17 | Palantir Technologies Inc. | Adaptive and transparent entity screening |
US10853454B2 (en) | 2014-03-21 | 2020-12-01 | Palantir Technologies Inc. | Provider portal |
US10970261B2 (en) | 2013-07-05 | 2021-04-06 | Palantir Technologies Inc. | System and method for data quality monitors |
US10977279B2 (en) | 2013-03-15 | 2021-04-13 | Palantir Technologies Inc. | Time-sensitive cube |
US11061542B1 (en) | 2018-06-01 | 2021-07-13 | Palantir Technologies Inc. | Systems and methods for determining and displaying optimal associations of data items |
US11061874B1 (en) | 2017-12-14 | 2021-07-13 | Palantir Technologies Inc. | Systems and methods for resolving entity data across various data structures |
US11074277B1 (en) | 2017-05-01 | 2021-07-27 | Palantir Technologies Inc. | Secure resolution of canonical entities |
US11106692B1 (en) | 2016-08-04 | 2021-08-31 | Palantir Technologies Inc. | Data record resolution and correlation system |
US11302426B1 (en) | 2015-01-02 | 2022-04-12 | Palantir Technologies Inc. | Unified data interface and system |
Families Citing this family (121)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9104695B1 (en) | 2009-07-27 | 2015-08-11 | Palantir Technologies, Inc. | Geotagging structured data |
US9547693B1 (en) | 2011-06-23 | 2017-01-17 | Palantir Technologies Inc. | Periodic database search manager for multiple data sources |
US9798768B2 (en) | 2012-09-10 | 2017-10-24 | Palantir Technologies, Inc. | Search around visual queries |
US9501507B1 (en) | 2012-12-27 | 2016-11-22 | Palantir Technologies Inc. | Geo-temporal indexing and searching |
US10275778B1 (en) | 2013-03-15 | 2019-04-30 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive investigation based on automatic malfeasance clustering of related data in various data structures |
US8799799B1 (en) | 2013-05-07 | 2014-08-05 | Palantir Technologies Inc. | Interactive geospatial map |
US9830325B1 (en) * | 2013-09-11 | 2017-11-28 | Intuit Inc. | Determining a likelihood that two entities are the same |
US9785317B2 (en) | 2013-09-24 | 2017-10-10 | Palantir Technologies Inc. | Presentation and analysis of user interaction data |
US8812960B1 (en) | 2013-10-07 | 2014-08-19 | Palantir Technologies Inc. | Cohort-based presentation of user interaction data |
US9116975B2 (en) | 2013-10-18 | 2015-08-25 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive simultaneous querying of multiple data stores |
US9734217B2 (en) | 2013-12-16 | 2017-08-15 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US10356032B2 (en) | 2013-12-26 | 2019-07-16 | Palantir Technologies Inc. | System and method for detecting confidential information emails |
US8832832B1 (en) | 2014-01-03 | 2014-09-09 | Palantir Technologies Inc. | IP reputation |
IN2014MU00169A (en) * | 2014-01-17 | 2015-08-28 | Tata Consultancy Services Ltd | |
US11263646B1 (en) * | 2014-03-31 | 2022-03-01 | Groupon, Inc. | Systems, apparatus, and methods of programmatically determining unique contacts |
US9904959B2 (en) * | 2014-06-09 | 2018-02-27 | Verifi, Inc. | Descriptor exchange |
US9619557B2 (en) | 2014-06-30 | 2017-04-11 | Palantir Technologies, Inc. | Systems and methods for key phrase characterization of documents |
US9535974B1 (en) | 2014-06-30 | 2017-01-03 | Palantir Technologies Inc. | Systems and methods for identifying key phrase clusters within documents |
US9129219B1 (en) | 2014-06-30 | 2015-09-08 | Palantir Technologies, Inc. | Crime risk forecasting |
US9256664B2 (en) | 2014-07-03 | 2016-02-09 | Palantir Technologies Inc. | System and method for news events detection and visualization |
US10572935B1 (en) * | 2014-07-16 | 2020-02-25 | Intuit, Inc. | Disambiguation of entities based on financial interactions |
US20160026923A1 (en) | 2014-07-22 | 2016-01-28 | Palantir Technologies Inc. | System and method for determining a propensity of entity to take a specified action |
US9390086B2 (en) | 2014-09-11 | 2016-07-12 | Palantir Technologies Inc. | Classification system with methodology for efficient verification |
US9501851B2 (en) | 2014-10-03 | 2016-11-22 | Palantir Technologies Inc. | Time-series analysis system |
US9767172B2 (en) | 2014-10-03 | 2017-09-19 | Palantir Technologies Inc. | Data aggregation and analysis system |
US9785328B2 (en) | 2014-10-06 | 2017-10-10 | Palantir Technologies Inc. | Presentation of multivariate data on a graphical user interface of a computing system |
US9229952B1 (en) | 2014-11-05 | 2016-01-05 | Palantir Technologies, Inc. | History preserving data pipeline system and method |
US9043894B1 (en) | 2014-11-06 | 2015-05-26 | Palantir Technologies Inc. | Malicious software detection in a computing system |
US10108623B2 (en) | 2014-12-12 | 2018-10-23 | International Business Machines Corporation | Merging database operations for serializable transaction execution |
US10552994B2 (en) | 2014-12-22 | 2020-02-04 | Palantir Technologies Inc. | Systems and interactive user interfaces for dynamic retrieval, analysis, and triage of data items |
US9348920B1 (en) | 2014-12-22 | 2016-05-24 | Palantir Technologies Inc. | Concept indexing among database of documents using machine learning techniques |
US10362133B1 (en) | 2014-12-22 | 2019-07-23 | Palantir Technologies Inc. | Communication data processing architecture |
US10452651B1 (en) | 2014-12-23 | 2019-10-22 | Palantir Technologies Inc. | Searching charts |
US9335911B1 (en) | 2014-12-29 | 2016-05-10 | Palantir Technologies Inc. | Interactive user interface for dynamic data analysis exploration and query processing |
US9817563B1 (en) | 2014-12-29 | 2017-11-14 | Palantir Technologies Inc. | System and method of generating data points from one or more data stores of data items for chart creation and manipulation |
US9727560B2 (en) | 2015-02-25 | 2017-08-08 | Palantir Technologies Inc. | Systems and methods for organizing and identifying documents via hierarchies and dimensions of tags |
EP3070622A1 (en) | 2015-03-16 | 2016-09-21 | Palantir Technologies, Inc. | Interactive user interfaces for location-based data analysis |
US9886467B2 (en) | 2015-03-19 | 2018-02-06 | Plantir Technologies Inc. | System and method for comparing and visualizing data entities and data entity series |
US9348880B1 (en) | 2015-04-01 | 2016-05-24 | Palantir Technologies, Inc. | Federated search of multiple sources with conflict resolution |
US10733157B1 (en) * | 2015-06-26 | 2020-08-04 | Groupon, Inc. | Hybrid data integration platform |
US10387882B2 (en) | 2015-07-01 | 2019-08-20 | Klarna Ab | Method for using supervised model with physical store |
US9904916B2 (en) | 2015-07-01 | 2018-02-27 | Klarna Ab | Incremental login and authentication to user portal without username/password |
US9996595B2 (en) | 2015-08-03 | 2018-06-12 | Palantir Technologies, Inc. | Providing full data provenance visualization for versioned datasets |
US9456000B1 (en) | 2015-08-06 | 2016-09-27 | Palantir Technologies Inc. | Systems, methods, user interfaces, and computer-readable media for investigating potential malicious communications |
US9600146B2 (en) | 2015-08-17 | 2017-03-21 | Palantir Technologies Inc. | Interactive geospatial map |
US9671776B1 (en) | 2015-08-20 | 2017-06-06 | Palantir Technologies Inc. | Quantifying, tracking, and anticipating risk at a manufacturing facility, taking deviation type and staffing conditions into account |
US11150917B2 (en) | 2015-08-26 | 2021-10-19 | Palantir Technologies Inc. | System for data aggregation and analysis of data from a plurality of data sources |
US9485265B1 (en) | 2015-08-28 | 2016-11-01 | Palantir Technologies Inc. | Malicious activity detection system capable of efficiently processing data accessed from databases and generating alerts for display in interactive user interfaces |
US10496716B2 (en) | 2015-08-31 | 2019-12-03 | Microsoft Technology Licensing, Llc | Discovery of network based data sources for ingestion and recommendations |
US10706434B1 (en) | 2015-09-01 | 2020-07-07 | Palantir Technologies Inc. | Methods and systems for determining location information |
US9639580B1 (en) | 2015-09-04 | 2017-05-02 | Palantir Technologies, Inc. | Computer-implemented systems and methods for data management and visualization |
US9576015B1 (en) | 2015-09-09 | 2017-02-21 | Palantir Technologies, Inc. | Domain-specific language for dataset transformations |
US20170069002A1 (en) * | 2015-09-09 | 2017-03-09 | Mastercard International Incorporated | Systems and Methods for Identifying Aggregate Merchants |
US9424669B1 (en) | 2015-10-21 | 2016-08-23 | Palantir Technologies Inc. | Generating graphical representations of event participation flow |
US10235725B2 (en) * | 2015-11-09 | 2019-03-19 | Mastercard International Incorporated | Method and system for determining merchant gratuity values |
US10223429B2 (en) * | 2015-12-01 | 2019-03-05 | Palantir Technologies Inc. | Entity data attribution using disparate data sets |
US10706056B1 (en) | 2015-12-02 | 2020-07-07 | Palantir Technologies Inc. | Audit log report generator |
US10114884B1 (en) | 2015-12-16 | 2018-10-30 | Palantir Technologies Inc. | Systems and methods for attribute analysis of one or more databases |
US20170177655A1 (en) * | 2015-12-17 | 2017-06-22 | SpringAhead, Inc. | Dynamic data normalization and duplicate analysis |
US10373099B1 (en) | 2015-12-18 | 2019-08-06 | Palantir Technologies Inc. | Misalignment detection system for efficiently processing database-stored data and automatically generating misalignment information for display in interactive user interfaces |
US10871878B1 (en) | 2015-12-29 | 2020-12-22 | Palantir Technologies Inc. | System log analysis and object user interaction correlation system |
US9792020B1 (en) | 2015-12-30 | 2017-10-17 | Palantir Technologies Inc. | Systems for collecting, aggregating, and storing data, generating interactive user interfaces for analyzing data, and generating alerts based upon collected data |
US10698938B2 (en) | 2016-03-18 | 2020-06-30 | Palantir Technologies Inc. | Systems and methods for organizing and identifying documents via hierarchies and dimensions of tags |
US9652139B1 (en) | 2016-04-06 | 2017-05-16 | Palantir Technologies Inc. | Graphical representation of an output |
US10068199B1 (en) | 2016-05-13 | 2018-09-04 | Palantir Technologies Inc. | System to catalogue tracking data |
US10007674B2 (en) | 2016-06-13 | 2018-06-26 | Palantir Technologies Inc. | Data revision control in large-scale data analytic systems |
US10545975B1 (en) | 2016-06-22 | 2020-01-28 | Palantir Technologies Inc. | Visual analysis of data using sequenced dataset reduction |
US10909130B1 (en) | 2016-07-01 | 2021-02-02 | Palantir Technologies Inc. | Graphical user interface for a database system |
US10552002B1 (en) | 2016-09-27 | 2020-02-04 | Palantir Technologies Inc. | User interface based variable machine modeling |
US10726507B1 (en) | 2016-11-11 | 2020-07-28 | Palantir Technologies Inc. | Graphical representation of a complex task |
US10318630B1 (en) | 2016-11-21 | 2019-06-11 | Palantir Technologies Inc. | Analysis of large bodies of textual data |
US9842338B1 (en) | 2016-11-21 | 2017-12-12 | Palantir Technologies Inc. | System to identify vulnerable card readers |
US11250425B1 (en) | 2016-11-30 | 2022-02-15 | Palantir Technologies Inc. | Generating a statistic using electronic transaction data |
US9886525B1 (en) | 2016-12-16 | 2018-02-06 | Palantir Technologies Inc. | Data item aggregate probability analysis system |
GB201621434D0 (en) | 2016-12-16 | 2017-02-01 | Palantir Technologies Inc | Processing sensor logs |
US10249033B1 (en) | 2016-12-20 | 2019-04-02 | Palantir Technologies Inc. | User interface for managing defects |
US10728262B1 (en) | 2016-12-21 | 2020-07-28 | Palantir Technologies Inc. | Context-aware network-based malicious activity warning systems |
US11373752B2 (en) | 2016-12-22 | 2022-06-28 | Palantir Technologies Inc. | Detection of misuse of a benefit system |
US10360238B1 (en) | 2016-12-22 | 2019-07-23 | Palantir Technologies Inc. | Database systems and user interfaces for interactive data association, analysis, and presentation |
US10721262B2 (en) | 2016-12-28 | 2020-07-21 | Palantir Technologies Inc. | Resource-centric network cyber attack warning system |
US10762471B1 (en) | 2017-01-09 | 2020-09-01 | Palantir Technologies Inc. | Automating management of integrated workflows based on disparate subsidiary data sources |
US10133621B1 (en) | 2017-01-18 | 2018-11-20 | Palantir Technologies Inc. | Data analysis system to facilitate investigative process |
US10509844B1 (en) | 2017-01-19 | 2019-12-17 | Palantir Technologies Inc. | Network graph parser |
US10515109B2 (en) | 2017-02-15 | 2019-12-24 | Palantir Technologies Inc. | Real-time auditing of industrial equipment condition |
US10581954B2 (en) | 2017-03-29 | 2020-03-03 | Palantir Technologies Inc. | Metric collection and aggregation for distributed software services |
US10866936B1 (en) | 2017-03-29 | 2020-12-15 | Palantir Technologies Inc. | Model object management and storage system |
US10133783B2 (en) | 2017-04-11 | 2018-11-20 | Palantir Technologies Inc. | Systems and methods for constraint driven database searching |
US10606872B1 (en) | 2017-05-22 | 2020-03-31 | Palantir Technologies Inc. | Graphical user interface for a database system |
US10795749B1 (en) | 2017-05-31 | 2020-10-06 | Palantir Technologies Inc. | Systems and methods for providing fault analysis user interface |
US10956406B2 (en) | 2017-06-12 | 2021-03-23 | Palantir Technologies Inc. | Propagated deletion of database records and derived data |
US10572835B2 (en) * | 2017-07-13 | 2020-02-25 | Microsoft Technology Licensing, Llc | Machine-learning algorithm for talent peer determinations |
US11216762B1 (en) | 2017-07-13 | 2022-01-04 | Palantir Technologies Inc. | Automated risk visualization using customer-centric data analysis |
US10430444B1 (en) | 2017-07-24 | 2019-10-01 | Palantir Technologies Inc. | Interactive geospatial map and geospatial visualization systems |
US11281726B2 (en) | 2017-12-01 | 2022-03-22 | Palantir Technologies Inc. | System and methods for faster processor comparisons of visual graph features |
US10769171B1 (en) | 2017-12-07 | 2020-09-08 | Palantir Technologies Inc. | Relationship analysis and mapping for interrelated multi-layered datasets |
US10877984B1 (en) | 2017-12-07 | 2020-12-29 | Palantir Technologies Inc. | Systems and methods for filtering and visualizing large scale datasets |
US10783162B1 (en) | 2017-12-07 | 2020-09-22 | Palantir Technologies Inc. | Workflow assistant |
US11314721B1 (en) | 2017-12-07 | 2022-04-26 | Palantir Technologies Inc. | User-interactive defect analysis for root cause |
US10541881B2 (en) * | 2017-12-14 | 2020-01-21 | Disney Enterprises, Inc. | Automated network supervision including detecting an anonymously administered node, identifying the administrator of the anonymously administered node, and registering the administrator and the anonymously administered node |
US11263382B1 (en) | 2017-12-22 | 2022-03-01 | Palantir Technologies Inc. | Data normalization and irregularity detection system |
US11494687B2 (en) | 2018-03-05 | 2022-11-08 | Yodlee, Inc. | Generating samples of transaction data sets |
US10877654B1 (en) | 2018-04-03 | 2020-12-29 | Palantir Technologies Inc. | Graphical user interfaces for optimizations |
US10754822B1 (en) | 2018-04-18 | 2020-08-25 | Palantir Technologies Inc. | Systems and methods for ontology migration |
US10885021B1 (en) | 2018-05-02 | 2021-01-05 | Palantir Technologies Inc. | Interactive interpreter and graphical user interface |
US10754946B1 (en) | 2018-05-08 | 2020-08-25 | Palantir Technologies Inc. | Systems and methods for implementing a machine learning approach to modeling entity behavior |
US11119630B1 (en) | 2018-06-19 | 2021-09-14 | Palantir Technologies Inc. | Artificial intelligence assisted evaluations and user interface for same |
US11250501B2 (en) * | 2018-08-21 | 2022-02-15 | Capital One Services, Llc | Scalable architecture for managing transactions |
US11164245B1 (en) * | 2018-08-28 | 2021-11-02 | Intuit Inc. | Method and system for identifying characteristics of transaction strings with an attention based recurrent neural network |
US11126638B1 (en) | 2018-09-13 | 2021-09-21 | Palantir Technologies Inc. | Data visualization and parsing system |
US20200098053A1 (en) * | 2018-09-26 | 2020-03-26 | Intuit Inc. | Method and system for user data driven financial transaction description dictionary construction |
US10572607B1 (en) * | 2018-09-27 | 2020-02-25 | Intuit Inc. | Translating transaction descriptions using machine learning |
US11294928B1 (en) | 2018-10-12 | 2022-04-05 | Palantir Technologies Inc. | System architecture for relating and linking data objects |
US20200160326A1 (en) | 2018-11-15 | 2020-05-21 | Paypal, Inc. | System and method for optimizing data writing to a blockchain |
US10891631B2 (en) * | 2018-12-10 | 2021-01-12 | Paypal, Inc. | Framework for generating risk evaluation models |
CN109829804A (en) * | 2019-01-10 | 2019-05-31 | 西安交通大学 | A kind of tax risk recognition methods towards marker samples missing administrative region |
CN110175236B (en) * | 2019-04-24 | 2023-07-21 | 平安科技(深圳)有限公司 | Training sample generation method and device for text classification and computer equipment |
US20210217014A1 (en) * | 2020-01-09 | 2021-07-15 | Visa International Service Association | Method, System, and Computer Program Product for Co-Located Merchant Anomaly Detection |
EP3923154A1 (en) * | 2020-06-11 | 2021-12-15 | Ivalua Sas | Identifying and generating links between data |
US11392769B2 (en) * | 2020-07-15 | 2022-07-19 | Fmr Llc | Systems and methods for expert driven document identification |
WO2022060809A1 (en) * | 2020-09-17 | 2022-03-24 | Mastercard International Incorporated | Continuous learning for seller disambiguation, assessment, and onboarding to electronic marketplaces |
WO2022124913A1 (en) * | 2020-12-10 | 2022-06-16 | Xero Limited | Systems and methods for improved transaction reconciliation |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20110099133A1 (en) * | 2009-10-28 | 2011-04-28 | Industrial Technology Research Institute | Systems and methods for capturing and managing collective social intelligence information |
CN102054015A (en) * | 2009-10-28 | 2011-05-11 | 财团法人工业技术研究院 | System and method of organizing community intelligent information by using organic matter data model |
US20130166480A1 (en) * | 2011-12-21 | 2013-06-27 | Telenav, Inc. | Navigation system with point of interest classification mechanism and method of operation thereof |
Family Cites Families (411)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5241625A (en) | 1990-11-27 | 1993-08-31 | Farallon Computing, Inc. | Screen image sharing among heterogeneous computers |
US6101479A (en) | 1992-07-15 | 2000-08-08 | Shaw; James G. | System and method for allocating company resources to fulfill customer expectations |
US5819226A (en) | 1992-09-08 | 1998-10-06 | Hnc Software Inc. | Fraud detection using predictive modeling |
US5892900A (en) | 1996-08-30 | 1999-04-06 | Intertrust Technologies Corp. | Systems and methods for secure transaction management and electronic rights protection |
US5777549A (en) | 1995-03-29 | 1998-07-07 | Cabletron Systems, Inc. | Method and apparatus for policy-based alarm notification in a distributed network management environment |
US5832218A (en) | 1995-12-14 | 1998-11-03 | International Business Machines Corporation | Client/server electronic mail system for providng off-line client utilization and seamless server resynchronization |
US6006242A (en) | 1996-04-05 | 1999-12-21 | Bankers Systems, Inc. | Apparatus and method for dynamically creating a document |
US8725493B2 (en) | 2004-01-06 | 2014-05-13 | Neuric Llc | Natural language parsing method to provide conceptual flow |
US5845300A (en) | 1996-06-05 | 1998-12-01 | Microsoft Corporation | Method and apparatus for suggesting completions for a partially entered data item based on previously-entered, associated data items |
US6094643A (en) | 1996-06-14 | 2000-07-25 | Card Alert Services, Inc. | System for detecting counterfeit financial card fraud |
US5897636A (en) | 1996-07-11 | 1999-04-27 | Tandem Corporation Incorporated | Distributed object computer system with hierarchical name space versioning |
US5878434A (en) | 1996-07-18 | 1999-03-02 | Novell, Inc | Transaction clash management in a disconnectable computer and network |
US5826021A (en) | 1996-09-17 | 1998-10-20 | Sun Microsystems, Inc. | Disconnected write authorization in a client/server computing system |
US5870559A (en) | 1996-10-15 | 1999-02-09 | Mercury Interactive | Software system and associated methods for facilitating the analysis and management of web sites |
CA2190043C (en) | 1996-11-12 | 2001-10-16 | Don E. Hameluck | Buffered screen capturing software tool usability testing of computer applications |
US6430305B1 (en) * | 1996-12-20 | 2002-08-06 | Synaptics, Incorporated | Identity verification methods |
US6065026A (en) | 1997-01-09 | 2000-05-16 | Document.Com, Inc. | Multi-user electronic document authoring system with prompted updating of shared language |
US5966706A (en) | 1997-02-19 | 1999-10-12 | At&T Corp | Local logging in a distributed database management computer system |
US6026233A (en) | 1997-05-27 | 2000-02-15 | Microsoft Corporation | Method and apparatus for presenting and selecting options to modify a programming language statement |
US7403922B1 (en) | 1997-07-28 | 2008-07-22 | Cybersource Corporation | Method and apparatus for evaluating fraud risk in an electronic commerce transaction |
US6463404B1 (en) | 1997-08-08 | 2002-10-08 | British Telecommunications Public Limited Company | Translation |
US6134582A (en) | 1998-05-26 | 2000-10-17 | Microsoft Corporation | System and method for managing electronic mail messages using a client-based database |
US7168039B2 (en) | 1998-06-02 | 2007-01-23 | International Business Machines Corporation | Method and system for reducing the horizontal space required for displaying a column containing text data |
US6243706B1 (en) | 1998-07-24 | 2001-06-05 | Avid Technology, Inc. | System and method for managing the creation and production of computer generated works |
US6243717B1 (en) | 1998-09-01 | 2001-06-05 | Camstar Systems, Inc. | System and method for implementing revision management of linked data entities and user dependent terminology |
US6232971B1 (en) | 1998-09-23 | 2001-05-15 | International Business Machines Corporation | Variable modality child windows |
US7213030B1 (en) | 1998-10-16 | 2007-05-01 | Jenkins Steven R | Web-enabled transaction and collaborative management system |
US6560578B2 (en) | 1999-03-12 | 2003-05-06 | Expanse Networks, Inc. | Advertisement selection system supporting discretionary target market characteristics |
US7418399B2 (en) | 1999-03-10 | 2008-08-26 | Illinois Institute Of Technology | Methods and kits for managing diagnosis and therapeutics of bacterial infections |
US7373592B2 (en) | 1999-07-30 | 2008-05-13 | Microsoft Corporation | Modeless child windows for application programs |
GB2371901B (en) | 1999-09-21 | 2004-06-23 | Andrew E Borthwick | A probabilistic record linkage model derived from training data |
US6523019B1 (en) | 1999-09-21 | 2003-02-18 | Choicemaker Technologies, Inc. | Probabilistic record linkage model derived from training data |
US6519627B1 (en) | 1999-09-27 | 2003-02-11 | International Business Machines Corporation | System and method for conducting disconnected transactions with service contracts for pervasive computing devices |
WO2001025906A1 (en) | 1999-10-01 | 2001-04-12 | Global Graphics Software Limited | Method and system for arranging a workflow using graphical user interface |
US6944821B1 (en) | 1999-12-07 | 2005-09-13 | International Business Machines Corporation | Copy/paste mechanism and paste buffer that includes source information for copied data |
US20040117387A1 (en) | 2000-02-25 | 2004-06-17 | Vincent Civetta | Database sizing and diagnostic utility |
US20020032677A1 (en) | 2000-03-01 | 2002-03-14 | Jeff Morgenthaler | Methods for creating, editing, and updating searchable graphical database and databases of graphical images and information and displaying graphical images from a searchable graphical database or databases in a sequential or slide show format |
JP2001283120A (en) | 2000-03-31 | 2001-10-12 | Oki Electric Ind Co Ltd | Transaction supporting system |
EP1290575B1 (en) | 2000-05-16 | 2005-06-08 | O'Carroll, Garrett | A document processing system and method |
US8386945B1 (en) | 2000-05-17 | 2013-02-26 | Eastman Kodak Company | System and method for implementing compound documents in a production printing workflow |
GB2366498A (en) | 2000-08-25 | 2002-03-06 | Copyn Ltd | Method of bookmarking a section of a web-page and storing said bookmarks |
US6795868B1 (en) | 2000-08-31 | 2004-09-21 | Data Junction Corp. | System and method for event-driven data transformation |
TWI244617B (en) | 2000-09-16 | 2005-12-01 | Ibm | A client/server-based data processing system for performing transactions between clients and a server and a method of performing the transactions |
US20020065708A1 (en) | 2000-09-22 | 2002-05-30 | Hikmet Senay | Method and system for interactive visual analyses of organizational interactions |
US8707185B2 (en) | 2000-10-10 | 2014-04-22 | Addnclick, Inc. | Dynamic information management system and method for content delivery and sharing in content-, metadata- and viewer-based, live social networking among users concurrently engaged in the same and/or similar content |
US8117281B2 (en) | 2006-11-02 | 2012-02-14 | Addnclick, Inc. | Using internet content as a means to establish live social networks by linking internet users to each other who are simultaneously engaged in the same and/or similar content |
US6754640B2 (en) * | 2000-10-30 | 2004-06-22 | William O. Bozeman | Universal positive pay match, authentication, authorization, settlement and clearing system |
US6978419B1 (en) | 2000-11-15 | 2005-12-20 | Justsystem Corporation | Method and apparatus for efficient identification of duplicate and near-duplicate documents and text spans using high-discriminability text fragments |
US7058648B1 (en) | 2000-12-01 | 2006-06-06 | Oracle International Corporation | Hierarchy-based secured document repository |
US20020103705A1 (en) * | 2000-12-06 | 2002-08-01 | Forecourt Communication Group | Method and apparatus for using prior purchases to select activities to present to a customer |
US7529698B2 (en) * | 2001-01-16 | 2009-05-05 | Raymond Anthony Joao | Apparatus and method for providing transaction history information, account history information, and/or charge-back information |
US9053222B2 (en) | 2002-05-17 | 2015-06-09 | Lawrence A. Lynn | Patient safety processor |
US7921123B2 (en) | 2001-02-20 | 2011-04-05 | Hartford Fire Insurance Company | Method and system for processing physician claims over a network |
US7809650B2 (en) | 2003-07-01 | 2010-10-05 | Visa U.S.A. Inc. | Method and system for providing risk information in connection with transaction processing |
US20100057622A1 (en) * | 2001-02-27 | 2010-03-04 | Faith Patrick L | Distributed Quantum Encrypted Pattern Generation And Scoring |
US6980984B1 (en) | 2001-05-16 | 2005-12-27 | Kanisa, Inc. | Content provider systems and methods using structured data |
US7877421B2 (en) | 2001-05-25 | 2011-01-25 | International Business Machines Corporation | Method and system for mapping enterprise data assets to a semantic information model |
US20040205492A1 (en) | 2001-07-26 | 2004-10-14 | Newsome Mark R. | Content clipping service |
US20030036927A1 (en) | 2001-08-20 | 2003-02-20 | Bowen Susan W. | Healthcare information search system and user interface |
US20030061132A1 (en) | 2001-09-26 | 2003-03-27 | Yu, Mason K. | System and method for categorizing, aggregating and analyzing payment transactions data |
US7058567B2 (en) | 2001-10-10 | 2006-06-06 | Xerox Corporation | Natural language parser |
US6877136B2 (en) | 2001-10-26 | 2005-04-05 | United Services Automobile Association (Usaa) | System and method of providing electronic access to one or more documents |
US7756728B2 (en) | 2001-10-31 | 2010-07-13 | Siemens Medical Solutions Usa, Inc. | Healthcare system and user interface for consolidating patient related information from different sources |
US6799173B2 (en) | 2001-11-14 | 2004-09-28 | Sun Microsystems, Inc. | Method and apparatus for sharing code containing references to non-shared objects |
US7089541B2 (en) | 2001-11-30 | 2006-08-08 | Sun Microsystems, Inc. | Modular parser architecture with mini parsers |
US7475242B2 (en) | 2001-12-18 | 2009-01-06 | Hewlett-Packard Development Company, L.P. | Controlling the distribution of information |
US7174377B2 (en) | 2002-01-16 | 2007-02-06 | Xerox Corporation | Method and apparatus for collaborative document versioning of networked documents |
US7225183B2 (en) | 2002-01-28 | 2007-05-29 | Ipxl, Inc. | Ontology-based information management system and method |
US7813937B1 (en) | 2002-02-15 | 2010-10-12 | Fair Isaac Corporation | Consistency modeling of healthcare claims to detect fraud and abuse |
US20030171942A1 (en) | 2002-03-06 | 2003-09-11 | I-Centrix Llc | Contact relationship management system and method |
US6993539B2 (en) | 2002-03-19 | 2006-01-31 | Network Appliance, Inc. | System and method for determining changes in two snapshots and for transmitting changes to destination snapshot |
EP1493113A4 (en) * | 2002-03-20 | 2009-04-22 | Catalina Marketing Corp | Targeted incentives based upon predicted behavior |
US7533026B2 (en) | 2002-04-12 | 2009-05-12 | International Business Machines Corporation | Facilitating management of service elements usable in providing information technology service offerings |
US7426559B2 (en) | 2002-05-09 | 2008-09-16 | International Business Machines Corporation | Method for sequential coordination of external database application events with asynchronous internal database events |
US7539680B2 (en) | 2002-05-10 | 2009-05-26 | Lsi Corporation | Revision control for database of evolved design |
US8232725B1 (en) | 2002-05-21 | 2012-07-31 | Imaging Systems Technology | Plasma-tube gas discharge device |
US20040044648A1 (en) | 2002-06-24 | 2004-03-04 | Xmyphonic System As | Method for data-centric collaboration |
US6996583B2 (en) | 2002-07-01 | 2006-02-07 | International Business Machines Corporation | Real-time database update transaction with disconnected relational database clients |
US20040006523A1 (en) | 2002-07-08 | 2004-01-08 | Coker Don W. | System and method for preventing financial fraud |
US7461158B2 (en) | 2002-08-07 | 2008-12-02 | Intelliden, Inc. | System and method for controlling access rights to network resources |
US7076508B2 (en) | 2002-08-12 | 2006-07-11 | International Business Machines Corporation | Method, system, and program for merging log entries from multiple recovery log files |
US8799023B2 (en) | 2002-10-18 | 2014-08-05 | Medimpact Healthcare Systems, Inc. | Mass customization for management of healthcare |
GB0224589D0 (en) | 2002-10-22 | 2002-12-04 | British Telecomm | Method and system for processing or searching user records |
US20040083466A1 (en) | 2002-10-29 | 2004-04-29 | Dapp Michael C. | Hardware parser accelerator |
US20040088177A1 (en) | 2002-11-04 | 2004-05-06 | Electronic Data Systems Corporation | Employee performance management method and system |
AU2003298616A1 (en) | 2002-11-06 | 2004-06-03 | International Business Machines Corporation | Confidential data sharing and anonymous entity resolution |
EP1567929A2 (en) | 2002-11-15 | 2005-08-31 | Creo Inc. | Methods and systems for sharing data |
US20040111480A1 (en) | 2002-12-09 | 2004-06-10 | Yue Jonathan Zhanjun | Message screening system and method |
US8589273B2 (en) | 2002-12-23 | 2013-11-19 | Ge Corporate Financial Services, Inc. | Methods and systems for managing risk management information |
US7752117B2 (en) | 2003-01-31 | 2010-07-06 | Trading Technologies International, Inc. | System and method for money management in electronic trading environment |
US7403942B1 (en) | 2003-02-04 | 2008-07-22 | Seisint, Inc. | Method and system for processing data records |
US7912842B1 (en) | 2003-02-04 | 2011-03-22 | Lexisnexis Risk Data Management Inc. | Method and system for processing and linking data records |
US20040153418A1 (en) | 2003-02-05 | 2004-08-05 | Hanweck Gerald Alfred | System and method for providing access to data from proprietary tools |
US8386377B1 (en) | 2003-05-12 | 2013-02-26 | Id Analytics, Inc. | System and method for credit scoring using an identity network connectivity |
US8412566B2 (en) * | 2003-07-08 | 2013-04-02 | Yt Acquisition Corporation | High-precision customer-based targeting by individual usage statistics |
AU2003903994A0 (en) | 2003-07-31 | 2003-08-14 | Canon Kabushiki Kaisha | Collaborative editing with automatic layout |
US20090132347A1 (en) * | 2003-08-12 | 2009-05-21 | Russell Wayne Anderson | Systems And Methods For Aggregating And Utilizing Retail Transaction Records At The Customer Level |
WO2005036319A2 (en) * | 2003-09-22 | 2005-04-21 | Catalina Marketing International, Inc. | Assumed demographics, predicted behaviour, and targeted incentives |
US7584172B2 (en) | 2003-10-16 | 2009-09-01 | Sap Ag | Control for selecting data query and visual configuration |
US20050091186A1 (en) | 2003-10-24 | 2005-04-28 | Alon Elish | Integrated method and apparatus for capture, storage, and retrieval of information |
US8627489B2 (en) | 2003-10-31 | 2014-01-07 | Adobe Systems Incorporated | Distributed document version control |
US7080104B2 (en) | 2003-11-07 | 2006-07-18 | Plaxo, Inc. | Synchronization and merge engines |
US20050131935A1 (en) | 2003-11-18 | 2005-06-16 | O'leary Paul J. | Sector content mining system using a modular knowledge base |
US20050125715A1 (en) | 2003-12-04 | 2005-06-09 | Fabrizio Di Franco | Method of saving data in a graphical user interface |
US6948656B2 (en) | 2003-12-23 | 2005-09-27 | First Data Corporation | System with GPS to manage risk of financial transactions |
US7917376B2 (en) | 2003-12-29 | 2011-03-29 | Montefiore Medical Center | System and method for monitoring patient care |
US20050154628A1 (en) | 2004-01-13 | 2005-07-14 | Illumen, Inc. | Automated management of business performance information |
US20050154769A1 (en) | 2004-01-13 | 2005-07-14 | Llumen, Inc. | Systems and methods for benchmarking business performance data against aggregated business performance data |
US7853533B2 (en) | 2004-03-02 | 2010-12-14 | The 41St Parameter, Inc. | Method and system for identifying users and detecting fraud by use of the internet |
US20060026120A1 (en) | 2004-03-24 | 2006-02-02 | Update Publications Lp | Method and system for collecting, processing, and distributing residential property data |
US20060031779A1 (en) | 2004-04-15 | 2006-02-09 | Citrix Systems, Inc. | Selectively sharing screen data |
US8041701B2 (en) | 2004-05-04 | 2011-10-18 | DG FastChannel, Inc | Enhanced graphical interfaces for displaying visual data |
US7689601B2 (en) | 2004-05-06 | 2010-03-30 | Oracle International Corporation | Achieving web documents using unique document locators |
US7415704B2 (en) | 2004-05-20 | 2008-08-19 | Sap Ag | Sharing objects in runtime systems |
US7587721B2 (en) | 2004-05-20 | 2009-09-08 | Sap Ag | Sharing objects in runtime systems |
AU2005248858B8 (en) | 2004-05-25 | 2011-05-26 | Google Llc | Electronic message source reputation information system |
WO2005116887A1 (en) | 2004-05-25 | 2005-12-08 | Arion Human Capital Limited | Data analysis and flow control system |
US20060010130A1 (en) | 2004-07-09 | 2006-01-12 | Avraham Leff | Method and apparatus for synchronizing client transactions executed by an autonomous client |
US7870487B2 (en) | 2004-07-29 | 2011-01-11 | International Business Machines Corporation | Inserting into a document a screen image of a computer software application |
US7552116B2 (en) | 2004-08-06 | 2009-06-23 | The Board Of Trustees Of The University Of Illinois | Method and system for extracting web query interfaces |
WO2006018843A2 (en) | 2004-08-16 | 2006-02-23 | Beinsync Ltd. | A system and method for the synchronization of data across multiple computing devices |
US7617232B2 (en) | 2004-09-02 | 2009-11-10 | Microsoft Corporation | Centralized terminology and glossary development |
US7493333B2 (en) | 2004-09-03 | 2009-02-17 | Biowisdom Limited | System and method for parsing and/or exporting data from one or more multi-relational ontologies |
US20060059423A1 (en) | 2004-09-13 | 2006-03-16 | Stefan Lehmann | Apparatus, system, and method for creating customized workflow documentation |
US20060080316A1 (en) | 2004-10-08 | 2006-04-13 | Meridio Ltd | Multiple indexing of an electronic document to selectively permit access to the content and metadata thereof |
US20060080139A1 (en) | 2004-10-08 | 2006-04-13 | Woodhaven Health Services | Preadmission health care cost and reimbursement estimation tool |
US8892571B2 (en) | 2004-10-12 | 2014-11-18 | International Business Machines Corporation | Systems for associating records in healthcare database with individuals |
US7739246B2 (en) | 2004-10-14 | 2010-06-15 | Microsoft Corporation | System and method of merging contacts |
US7757220B2 (en) | 2004-10-21 | 2010-07-13 | Discovery Machine, Inc. | Computer interchange of knowledge hierarchies |
US7797197B2 (en) | 2004-11-12 | 2010-09-14 | Amazon Technologies, Inc. | Method and system for analyzing the performance of affiliate sites |
US8938434B2 (en) | 2004-11-22 | 2015-01-20 | Intelius, Inc. | Household grouping based on public records |
US7899796B1 (en) | 2004-11-23 | 2011-03-01 | Andrew Borthwick | Batch automated blocking and record matching |
US20060178954A1 (en) | 2004-12-13 | 2006-08-10 | Rohit Thukral | Iterative asset reconciliation process |
US20060129746A1 (en) | 2004-12-14 | 2006-06-15 | Ithink, Inc. | Method and graphic interface for storing, moving, sending or printing electronic data to two or more locations, in two or more formats with a single save function |
US7451397B2 (en) | 2004-12-15 | 2008-11-11 | Microsoft Corporation | System and method for automatically completing spreadsheet formulas |
US9020887B2 (en) | 2004-12-21 | 2015-04-28 | Proofpoint, Inc. | Managing the status of documents in a distributed storage system |
US8700414B2 (en) | 2004-12-29 | 2014-04-15 | Sap Ag | System supported optimization of event resolution |
US20060143079A1 (en) | 2004-12-29 | 2006-06-29 | Jayanta Basak | Cross-channel customer matching |
US8271436B2 (en) | 2005-02-07 | 2012-09-18 | Mimosa Systems, Inc. | Retro-fitting synthetic full copies of data |
US20060190295A1 (en) | 2005-02-22 | 2006-08-24 | Richard Merkin | Systems and methods for assessing and optimizing healthcare administration |
US8091784B1 (en) * | 2005-03-09 | 2012-01-10 | Diebold, Incorporated | Banking system controlled responsive to data bearing records |
WO2006102270A2 (en) | 2005-03-22 | 2006-09-28 | Cooper Kim A | Performance motivation systems and methods for contact centers |
US20060218491A1 (en) | 2005-03-25 | 2006-09-28 | International Business Machines Corporation | System, method and program product for community review of documents |
US7596528B1 (en) | 2005-03-31 | 2009-09-29 | Trading Technologies International, Inc. | System and method for dynamically regulating order entry in an electronic trading environment |
US8145686B2 (en) | 2005-05-06 | 2012-03-27 | Microsoft Corporation | Maintenance of link level consistency between database and file system |
US7672968B2 (en) | 2005-05-12 | 2010-03-02 | Apple Inc. | Displaying a tooltip associated with a concurrently displayed database object |
US8161122B2 (en) | 2005-06-03 | 2012-04-17 | Messagemind, Inc. | System and method of dynamically prioritized electronic mail graphical user interface, and measuring email productivity and collaboration trends |
US20060277460A1 (en) | 2005-06-03 | 2006-12-07 | Scott Forstall | Webview applications |
US8341259B2 (en) | 2005-06-06 | 2012-12-25 | Adobe Systems Incorporated | ASP for web analytics including a real-time segmentation workbench |
EP1732034A1 (en) * | 2005-06-06 | 2006-12-13 | First Data Corporation | System and method for authorizing electronic payment transactions |
US7761379B2 (en) | 2005-06-24 | 2010-07-20 | Fair Isaac Corporation | Mass compromise/point of compromise analytic detection and compromised card portfolio management system |
WO2007004224A1 (en) | 2005-07-05 | 2007-01-11 | Mconfirm Ltd. | Improved location based authentication system |
US8429527B1 (en) | 2005-07-12 | 2013-04-23 | Open Text S.A. | Complex data merging, such as in a workflow application |
CA2615659A1 (en) | 2005-07-22 | 2007-05-10 | Yogesh Chunilal Rathod | Universal knowledge management and desktop search system |
US20070178501A1 (en) | 2005-12-06 | 2007-08-02 | Matthew Rabinowitz | System and method for integrating and validating genotypic, phenotypic and medical information into a database according to a standardized ontology |
US7421429B2 (en) | 2005-08-04 | 2008-09-02 | Microsoft Corporation | Generate blog context ranking using track-back weight, context weight and, cumulative comment weight |
US7529726B2 (en) | 2005-08-22 | 2009-05-05 | International Business Machines Corporation | XML sub-document versioning method in XML databases using record storages |
US8095866B2 (en) | 2005-09-09 | 2012-01-10 | Microsoft Corporation | Filtering user interface for a data summary table |
US7958147B1 (en) | 2005-09-13 | 2011-06-07 | James Luke Turner | Method for providing customized and automated security assistance, a document marking regime, and central tracking and control for sensitive or classified documents in electronic format |
US7941336B1 (en) | 2005-09-14 | 2011-05-10 | D2C Solutions, LLC | Segregation-of-duties analysis apparatus and method |
US8468441B2 (en) | 2005-09-15 | 2013-06-18 | Microsoft Corporation | Cross-application support of charts |
US7672833B2 (en) | 2005-09-22 | 2010-03-02 | Fair Isaac Corporation | Method and apparatus for automatic entity disambiguation |
US8306986B2 (en) | 2005-09-30 | 2012-11-06 | American Express Travel Related Services Company, Inc. | Method, system, and computer program product for linking customer information |
WO2007041709A1 (en) | 2005-10-04 | 2007-04-12 | Basepoint Analytics Llc | System and method of detecting fraud |
US20090168163A1 (en) | 2005-11-01 | 2009-07-02 | Global Bionic Optics Pty Ltd. | Optical lens systems |
EP2463354B1 (en) | 2005-12-08 | 2017-03-29 | National Institute for Materials Science | Phosphor, Process for producing the same, and luminescent device |
US20070136095A1 (en) | 2005-12-09 | 2007-06-14 | Arizona Board Of Regents On Behalf Of The University Of Arizona | Icon Queues for Workflow Management |
US7606844B2 (en) | 2005-12-19 | 2009-10-20 | Commvault Systems, Inc. | System and method for performing replication copy storage operations |
US8726144B2 (en) | 2005-12-23 | 2014-05-13 | Xerox Corporation | Interactive learning-based document annotation |
US7788296B2 (en) | 2005-12-29 | 2010-08-31 | Guidewire Software, Inc. | Method and apparatus for managing a computer-based address book for incident-related work |
US8712828B2 (en) | 2005-12-30 | 2014-04-29 | Accenture Global Services Limited | Churn prediction and management system |
US20070185867A1 (en) | 2006-02-03 | 2007-08-09 | Matteo Maga | Statistical modeling methods for determining customer distribution by churn probability within a customer population |
US7490298B2 (en) | 2006-04-12 | 2009-02-10 | International Business Machines Corporation | Creating documentation screenshots on demand |
US7756843B1 (en) | 2006-05-25 | 2010-07-13 | Juniper Networks, Inc. | Identifying and processing confidential information on network endpoints |
US7866542B2 (en) | 2006-06-08 | 2011-01-11 | International Business Machines Corporation | System and method for resolving identities that are indefinitely resolvable |
US9195985B2 (en) * | 2006-06-08 | 2015-11-24 | Iii Holdings 1, Llc | Method, system, and computer program product for customer-level data verification |
JP4218700B2 (en) | 2006-06-19 | 2009-02-04 | コニカミノルタビジネステクノロジーズ株式会社 | Image forming apparatus |
US7720789B2 (en) | 2006-06-23 | 2010-05-18 | International Business Machines Corporation | System and method of member unique names |
US7933955B2 (en) | 2006-07-11 | 2011-04-26 | Igor Khalatian | One-click universal screen sharing |
US7747562B2 (en) | 2006-08-15 | 2010-06-29 | International Business Machines Corporation | Virtual multidimensional datasets for enterprise software systems |
US8230332B2 (en) | 2006-08-30 | 2012-07-24 | Compsci Resources, Llc | Interactive user interface for converting unstructured documents |
US8054756B2 (en) | 2006-09-18 | 2011-11-08 | Yahoo! Inc. | Path discovery and analytics for network data |
US9183321B2 (en) | 2006-10-16 | 2015-11-10 | Oracle International Corporation | Managing compound XML documents in a repository |
US20080103798A1 (en) | 2006-10-25 | 2008-05-01 | Domenikos Steven D | Identity Protection |
US7792353B2 (en) | 2006-10-31 | 2010-09-07 | Hewlett-Packard Development Company, L.P. | Retraining a machine-learning classifier using re-labeled training samples |
US8229902B2 (en) | 2006-11-01 | 2012-07-24 | Ab Initio Technology Llc | Managing storage of individually accessible data units |
US20080109714A1 (en) | 2006-11-03 | 2008-05-08 | Sap Ag | Capturing screen information |
US7657497B2 (en) | 2006-11-07 | 2010-02-02 | Ebay Inc. | Online fraud prevention using genetic algorithm solution |
US7962495B2 (en) | 2006-11-20 | 2011-06-14 | Palantir Technologies, Inc. | Creating data in a data store using a dynamic ontology |
US7853614B2 (en) | 2006-11-27 | 2010-12-14 | Rapleaf, Inc. | Hierarchical, traceable, and association reputation assessment of email domains |
US8126848B2 (en) | 2006-12-07 | 2012-02-28 | Robert Edward Wagner | Automated method for identifying and repairing logical data discrepancies between database replicas in a database cluster |
US8117022B2 (en) | 2006-12-07 | 2012-02-14 | Linker Sheldon O | Method and system for machine understanding, knowledge, and conversation |
US8290838B1 (en) * | 2006-12-29 | 2012-10-16 | Amazon Technologies, Inc. | Indicating irregularities in online financial transactions |
WO2008092147A2 (en) | 2007-01-26 | 2008-07-31 | Information Resources, Inc. | Analytic platform |
US8171418B2 (en) | 2007-01-31 | 2012-05-01 | Salesforce.Com, Inc. | Method and system for presenting a visual representation of the portion of the sets of data that a query is expected to return |
US20080208735A1 (en) | 2007-02-22 | 2008-08-28 | American Expresstravel Related Services Company, Inc., A New York Corporation | Method, System, and Computer Program Product for Managing Business Customer Contacts |
US7873557B2 (en) | 2007-02-28 | 2011-01-18 | Aaron Guidotti | Information, document, and compliance management for financial professionals, clients, and supervisors |
US8180717B2 (en) | 2007-03-20 | 2012-05-15 | President And Fellows Of Harvard College | System for estimating a distribution of message content categories in source data |
US8036971B2 (en) | 2007-03-30 | 2011-10-11 | Palantir Technologies, Inc. | Generating dynamic date sets that represent market conditions |
US20080255973A1 (en) | 2007-04-10 | 2008-10-16 | Robert El Wade | Sales transaction analysis tool and associated method of use |
US20090164387A1 (en) | 2007-04-17 | 2009-06-25 | Semandex Networks Inc. | Systems and methods for providing semantically enhanced financial information |
US7880921B2 (en) | 2007-05-01 | 2011-02-01 | Michael Joseph Dattilo | Method and apparatus to digitally whiteout mistakes on a printed form |
US7962904B2 (en) | 2007-05-10 | 2011-06-14 | Microsoft Corporation | Dynamic parser |
US7840456B2 (en) * | 2007-05-30 | 2010-11-23 | Intuit Inc. | System and method for categorizing credit card transaction data |
US7930547B2 (en) | 2007-06-15 | 2011-04-19 | Alcatel-Lucent Usa Inc. | High accuracy bloom filter using partitioned hashing |
WO2009009623A1 (en) | 2007-07-09 | 2009-01-15 | Tailwalker Technologies, Inc. | Integrating a methodology management system with project tasks in a project management system |
US7966199B1 (en) | 2007-07-19 | 2011-06-21 | Intuit Inc. | Method and system for identification of geographic condition zones using aggregated claim data |
US8600872B1 (en) | 2007-07-27 | 2013-12-03 | Wells Fargo Bank, N.A. | System and method for detecting account compromises |
US8156166B2 (en) | 2007-08-06 | 2012-04-10 | Intuit Inc. | Method and apparatus for selecting a doctor based on an observed experience level |
US20130275186A1 (en) * | 2007-08-14 | 2013-10-17 | Visa U.S.A. Inc. | Merchant Benchmarking Tool |
US7761525B2 (en) | 2007-08-23 | 2010-07-20 | International Business Machines Corporation | System and method for providing improved time references in documents |
US20130066673A1 (en) | 2007-09-06 | 2013-03-14 | Digg, Inc. | Adapting thresholds |
WO2009039391A1 (en) | 2007-09-21 | 2009-03-26 | The Methodist Hospital System | Systems, methods and apparatuses for generating and using representations of individual or aggregate human medical data |
EP2051173A3 (en) | 2007-09-27 | 2009-08-12 | Magix Ag | System and method for dynamic content insertion from the internet into a multimedia work |
US8849728B2 (en) | 2007-10-01 | 2014-09-30 | Purdue Research Foundation | Visual analytics law enforcement tools |
US8484115B2 (en) | 2007-10-03 | 2013-07-09 | Palantir Technologies, Inc. | Object-oriented time series generator |
US20090094270A1 (en) | 2007-10-08 | 2009-04-09 | Alirez Baldomero J | Method of building a validation database |
US8554719B2 (en) | 2007-10-18 | 2013-10-08 | Palantir Technologies, Inc. | Resolving database entity information |
US8214308B2 (en) * | 2007-10-23 | 2012-07-03 | Sas Institute Inc. | Computer-implemented systems and methods for updating predictive models |
US7650310B2 (en) * | 2007-10-30 | 2010-01-19 | Intuit Inc. | Technique for reducing phishing |
US20090126020A1 (en) | 2007-11-09 | 2009-05-14 | Norton Richard Elliott | Engine for rule based content filtering |
US9898767B2 (en) | 2007-11-14 | 2018-02-20 | Panjiva, Inc. | Transaction facilitating marketplace platform |
US20090150868A1 (en) | 2007-12-10 | 2009-06-11 | Al Chakra | Method and System for Capturing Movie Shots at the Time of an Automated Graphical User Interface Test Failure |
US8270577B2 (en) | 2007-12-13 | 2012-09-18 | Verizon Patent And Licensing Inc. | Multiple visual voicemail mailboxes |
US8417715B1 (en) | 2007-12-19 | 2013-04-09 | Tilmann Bruckhaus | Platform independent plug-in methods and systems for data mining and analytics |
US8738486B2 (en) * | 2007-12-31 | 2014-05-27 | Mastercard International Incorporated | Methods and apparatus for implementing an ensemble merchant prediction system |
US8666976B2 (en) * | 2007-12-31 | 2014-03-04 | Mastercard International Incorporated | Methods and systems for implementing approximate string matching within a database |
US7925652B2 (en) * | 2007-12-31 | 2011-04-12 | Mastercard International Incorporated | Methods and systems for implementing approximate string matching within a database |
US8055633B2 (en) | 2008-01-21 | 2011-11-08 | International Business Machines Corporation | Method, system and computer program product for duplicate detection |
KR100915295B1 (en) | 2008-01-22 | 2009-09-03 | 성균관대학교산학협력단 | System and method for search service having a function of automatic classification of search results |
US20090199106A1 (en) | 2008-02-05 | 2009-08-06 | Sony Ericsson Mobile Communications Ab | Communication terminal including graphical bookmark manager |
US20090216562A1 (en) | 2008-02-22 | 2009-08-27 | Faulkner Judith R | Method and apparatus for accommodating diverse healthcare record centers |
US8473519B1 (en) | 2008-02-25 | 2013-06-25 | Cisco Technology, Inc. | Unified communication audit tool |
US7765489B1 (en) | 2008-03-03 | 2010-07-27 | Shah Shalin N | Presenting notifications related to a medical study on a toolbar |
US8191766B2 (en) * | 2008-03-04 | 2012-06-05 | Mastercard International Incorporated | Methods and systems for managing merchant identifiers |
US8856088B2 (en) | 2008-04-01 | 2014-10-07 | Microsoft Corporation | Application-managed file versioning |
US8121962B2 (en) * | 2008-04-25 | 2012-02-21 | Fair Isaac Corporation | Automated entity identification for efficient profiling in an event probability prediction system |
US20090282068A1 (en) | 2008-05-12 | 2009-11-12 | Shockro John J | Semantic packager |
WO2009149063A1 (en) | 2008-06-02 | 2009-12-10 | Azuki Systems, Inc. | Media mashup system |
US20090319515A1 (en) | 2008-06-02 | 2009-12-24 | Steven Minton | System and method for managing entity knowledgebases |
US20090307049A1 (en) * | 2008-06-05 | 2009-12-10 | Fair Isaac Corporation | Soft Co-Clustering of Data |
US8924469B2 (en) | 2008-06-05 | 2014-12-30 | Headwater Partners I Llc | Enterprise access control and accounting allocation for access networks |
US8301593B2 (en) | 2008-06-12 | 2012-10-30 | Gravic, Inc. | Mixed mode synchronous and asynchronous replication system |
US8860754B2 (en) | 2008-06-22 | 2014-10-14 | Tableau Software, Inc. | Methods and systems of automatically generating marks in a graphical view |
CN102150129A (en) | 2008-08-04 | 2011-08-10 | 奎德公司 | Entity performance analysis engines |
US9348499B2 (en) | 2008-09-15 | 2016-05-24 | Palantir Technologies, Inc. | Sharing objects that rely on local resources with outside servers |
US8417561B2 (en) | 2008-09-24 | 2013-04-09 | Bank Of America Corporation | Market dynamics |
CN101685449B (en) | 2008-09-26 | 2012-07-11 | 国际商业机器公司 | Method and system for connecting tables in a plurality of heterogeneous distributed databases |
US20100114887A1 (en) | 2008-10-17 | 2010-05-06 | Google Inc. | Textual Disambiguation Using Social Connections |
US8391584B2 (en) | 2008-10-20 | 2013-03-05 | Jpmorgan Chase Bank, N.A. | Method and system for duplicate check detection |
US7974943B2 (en) | 2008-10-30 | 2011-07-05 | Hewlett-Packard Development Company, L.P. | Building a synchronized target database |
US8306947B2 (en) | 2008-10-30 | 2012-11-06 | Hewlett-Packard Development Company, L.P. | Replication of operations on objects distributed in a storage system |
US20100131502A1 (en) | 2008-11-25 | 2010-05-27 | Fordham Bradley S | Cohort group generation and automatic updating |
US8204859B2 (en) | 2008-12-10 | 2012-06-19 | Commvault Systems, Inc. | Systems and methods for managing replicated database data |
US8719350B2 (en) | 2008-12-23 | 2014-05-06 | International Business Machines Corporation | Email addressee verification |
US10115153B2 (en) | 2008-12-31 | 2018-10-30 | Fair Isaac Corporation | Detection of compromise of merchants, ATMS, and networks |
US20100262688A1 (en) | 2009-01-21 | 2010-10-14 | Daniar Hussain | Systems, methods, and devices for detecting security vulnerabilities in ip networks |
US20100191563A1 (en) | 2009-01-23 | 2010-07-29 | Doctors' Administrative Solutions, Llc | Physician Practice Optimization Tracking |
US20110213655A1 (en) | 2009-01-24 | 2011-09-01 | Kontera Technologies, Inc. | Hybrid contextual advertising and related content analysis and display techniques |
US8073857B2 (en) | 2009-02-17 | 2011-12-06 | International Business Machines Corporation | Semantics-based data transformation over a wire in mashups |
US8473454B2 (en) | 2009-03-10 | 2013-06-25 | Xerox Corporation | System and method of on-demand document processing |
US20100235915A1 (en) | 2009-03-12 | 2010-09-16 | Nasir Memon | Using host symptoms, host roles, and/or host reputation for detection of host infection |
US20100306285A1 (en) | 2009-05-28 | 2010-12-02 | Arcsight, Inc. | Specifying a Parser Using a Properties File |
US9141911B2 (en) | 2009-05-29 | 2015-09-22 | Aspen Technology, Inc. | Apparatus and method for automated data selection in model identification and adaptation in multivariable process control |
US20100306029A1 (en) * | 2009-06-01 | 2010-12-02 | Ryan Jolley | Cardholder Clusters |
US8495151B2 (en) | 2009-06-05 | 2013-07-23 | Chandra Bodapati | Methods and systems for determining email addresses |
US20100313239A1 (en) | 2009-06-09 | 2010-12-09 | International Business Machines Corporation | Automated access control for rendered output |
US8554742B2 (en) * | 2009-07-06 | 2013-10-08 | Intelligent Medical Objects, Inc. | System and process for record duplication analysis |
EP2454661A1 (en) | 2009-07-15 | 2012-05-23 | Proviciel - Mlstate | System and method for creating a parser generator and associated computer program |
US8392556B2 (en) | 2009-07-16 | 2013-03-05 | Ca, Inc. | Selective reporting of upstream transaction trace data |
US9104695B1 (en) | 2009-07-27 | 2015-08-11 | Palantir Technologies, Inc. | Geotagging structured data |
US10242540B2 (en) | 2009-09-02 | 2019-03-26 | Fair Isaac Corporation | Visualization for payment card transaction fraud analysis |
US9280777B2 (en) | 2009-09-08 | 2016-03-08 | Target Brands, Inc. | Operations dashboard |
US20110066497A1 (en) | 2009-09-14 | 2011-03-17 | Choicestream, Inc. | Personalized advertising and recommendation |
US8214490B1 (en) | 2009-09-15 | 2012-07-03 | Symantec Corporation | Compact input compensating reputation data tracking mechanism |
CN102549390B (en) | 2009-09-30 | 2015-06-24 | 录象射流技术公司 | Thermal ink jet ink composition |
US20110078173A1 (en) | 2009-09-30 | 2011-03-31 | Avaya Inc. | Social Network User Interface |
US8595058B2 (en) * | 2009-10-15 | 2013-11-26 | Visa U.S.A. | Systems and methods to match identifiers |
US8321360B2 (en) | 2009-10-22 | 2012-11-27 | Symantec Corporation | Method and system for weighting transactions in a fraud detection system |
US9165304B2 (en) | 2009-10-23 | 2015-10-20 | Service Management Group, Inc. | Analyzing consumer behavior using electronically-captured consumer location data |
US11122009B2 (en) | 2009-12-01 | 2021-09-14 | Apple Inc. | Systems and methods for identifying geographic locations of social media content collected over social networks |
US20110131131A1 (en) | 2009-12-01 | 2011-06-02 | Bank Of America Corporation | Risk pattern determination and associated risk pattern alerts |
US8645478B2 (en) | 2009-12-10 | 2014-02-04 | Mcafee, Inc. | System and method for monitoring social engineering in a computer network environment |
US20110153384A1 (en) | 2009-12-17 | 2011-06-23 | Matthew Donald Horne | Visual comps builder |
EP2524299A4 (en) * | 2010-01-11 | 2013-11-13 | Panjiva Inc | Evaluating public records of supply transactions for financial investment decisions |
US9026552B2 (en) | 2010-01-18 | 2015-05-05 | Salesforce.Com, Inc. | System and method for linking contact records to company locations |
US20110208822A1 (en) | 2010-02-22 | 2011-08-25 | Yogesh Chunilal Rathod | Method and system for customized, contextual, dynamic and unified communication, zero click advertisement and prospective customers search engine |
US20110208565A1 (en) | 2010-02-23 | 2011-08-25 | Michael Ross | complex process management |
US8478709B2 (en) | 2010-03-08 | 2013-07-02 | Hewlett-Packard Development Company, L.P. | Evaluation of client status for likelihood of churn |
US8752054B2 (en) * | 2010-03-11 | 2014-06-10 | Avaya Inc. | Intelligent merging of transactions based on a variety of criteria |
US20110225482A1 (en) | 2010-03-15 | 2011-09-15 | Wizpatent Pte Ltd | Managing and generating citations in scholarly work |
US20110231296A1 (en) | 2010-03-16 | 2011-09-22 | UberMedia, Inc. | Systems and methods for interacting with messages, authors, and followers |
US20110231305A1 (en) | 2010-03-19 | 2011-09-22 | Visa U.S.A. Inc. | Systems and Methods to Identify Spending Patterns |
US8739118B2 (en) | 2010-04-08 | 2014-05-27 | Microsoft Corporation | Pragmatic mapping specification, compilation and validation |
US8306846B2 (en) | 2010-04-12 | 2012-11-06 | First Data Corporation | Transaction location analytics systems and methods |
US20110258216A1 (en) | 2010-04-20 | 2011-10-20 | International Business Machines Corporation | Usability enhancements for bookmarks of browsers |
US8874432B2 (en) | 2010-04-28 | 2014-10-28 | Nec Laboratories America, Inc. | Systems and methods for semi-supervised relationship extraction |
US8255399B2 (en) | 2010-04-28 | 2012-08-28 | Microsoft Corporation | Data classifier |
US8473415B2 (en) | 2010-05-04 | 2013-06-25 | Kevin Paul Siegel | System and method for identifying a point of compromise in a payment transaction processing system |
US20110289397A1 (en) | 2010-05-19 | 2011-11-24 | Mauricio Eastmond | Displaying Table Data in a Limited Display Area |
US20110295649A1 (en) | 2010-05-31 | 2011-12-01 | International Business Machines Corporation | Automatic churn prediction |
US8756224B2 (en) | 2010-06-16 | 2014-06-17 | Rallyverse, Inc. | Methods, systems, and media for content ranking using real-time data |
US8380719B2 (en) | 2010-06-18 | 2013-02-19 | Microsoft Corporation | Semantic content searching |
US8364642B1 (en) | 2010-07-07 | 2013-01-29 | Palantir Technologies, Inc. | Managing disconnected investigations |
US8407341B2 (en) | 2010-07-09 | 2013-03-26 | Bank Of America Corporation | Monitoring communications |
US8554653B2 (en) * | 2010-07-22 | 2013-10-08 | Visa International Service Association | Systems and methods to identify payment accounts having business spending activities |
US8775530B2 (en) | 2010-08-25 | 2014-07-08 | International Business Machines Corporation | Communication management method and system |
US20120065987A1 (en) | 2010-09-09 | 2012-03-15 | Siemens Medical Solutions Usa, Inc. | Computer-Based Patient Management for Healthcare |
US20120066166A1 (en) | 2010-09-10 | 2012-03-15 | International Business Machines Corporation | Predictive Analytics for Semi-Structured Case Oriented Processes |
US20120078595A1 (en) | 2010-09-24 | 2012-03-29 | Nokia Corporation | Method and apparatus for ontology matching |
US8549004B2 (en) | 2010-09-30 | 2013-10-01 | Hewlett-Packard Development Company, L.P. | Estimation of unique database values |
US8498998B2 (en) | 2010-10-11 | 2013-07-30 | International Business Machines Corporation | Grouping identity records to generate candidate lists to use in an entity and relationship resolution process |
WO2012054868A2 (en) | 2010-10-21 | 2012-04-26 | Visa International Service Association | Software and methods for risk and fraud mitigation |
US8949158B2 (en) | 2010-10-25 | 2015-02-03 | Intelius Inc. | Cost-sensitive alternating decision trees for record linkage |
JP5706137B2 (en) | 2010-11-22 | 2015-04-22 | インターナショナル・ビジネス・マシーンズ・コーポレーションInternational Business Machines Corporation | Method and computer program for displaying a plurality of posts (groups of data) on a computer screen in real time along a plurality of axes |
CA2817576C (en) | 2010-11-24 | 2016-06-07 | Logrhythm, Inc. | Scalable analytical processing of structured data |
CN102546446A (en) | 2010-12-13 | 2012-07-04 | 太仓市浏河镇亿网行网络技术服务部 | Email device |
US9141405B2 (en) | 2010-12-15 | 2015-09-22 | International Business Machines Corporation | User interface construction |
US8719166B2 (en) | 2010-12-16 | 2014-05-06 | Verizon Patent And Licensing Inc. | Iterative processing of transaction information to detect fraud |
US20120173381A1 (en) | 2011-01-03 | 2012-07-05 | Stanley Benjamin Smith | Process and system for pricing and processing weighted data in a federated or subscription based data source |
US20120197660A1 (en) | 2011-01-31 | 2012-08-02 | Ez Derm, Llc | Systems and methods to faciliate medical services |
US20120197657A1 (en) | 2011-01-31 | 2012-08-02 | Ez Derm, Llc | Systems and methods to facilitate medical services |
IL211163A0 (en) | 2011-02-10 | 2011-04-28 | Univ Ben Gurion | A method for generating a randomized data structure for representing sets, based on bloom filters |
KR101950529B1 (en) | 2011-02-24 | 2019-02-20 | 렉시스넥시스, 어 디비젼 오브 리드 엘서비어 인크. | Methods for electronic document searching and graphically representing electronic document searches |
WO2012119008A2 (en) | 2011-03-01 | 2012-09-07 | Early Warning Services, Llc | System and method for suspect entity detection and mitigation |
CA2830797A1 (en) | 2011-03-23 | 2012-09-27 | Detica Patent Limited | An automated fraud detection method and system |
US20120278249A1 (en) | 2011-04-29 | 2012-11-01 | American Express Travel Related Services Company, Inc. | Generating an Identity Theft Score |
US8861861B2 (en) * | 2011-05-10 | 2014-10-14 | Expensify, Inc. | System and method for processing receipts and other records of users |
US9104765B2 (en) | 2011-06-17 | 2015-08-11 | Robert Osann, Jr. | Automatic webpage characterization and search results annotation |
US8533165B2 (en) | 2011-07-03 | 2013-09-10 | Microsoft Corporation | Conflict resolution via metadata examination |
US8726379B1 (en) | 2011-07-15 | 2014-05-13 | Norse Corporation | Systems and methods for dynamic protection from electronic attacks |
US8982130B2 (en) | 2011-07-15 | 2015-03-17 | Green Charge Networks | Cluster mapping to highlight areas of electrical congestion |
US9996807B2 (en) | 2011-08-17 | 2018-06-12 | Roundhouse One Llc | Multidimensional digital platform for building integration and analysis |
US8732574B2 (en) | 2011-08-25 | 2014-05-20 | Palantir Technologies, Inc. | System and method for parameterizing documents for automatic workflow generation |
US8630892B2 (en) | 2011-08-31 | 2014-01-14 | Accenture Global Services Limited | Churn analysis system |
US8949164B1 (en) | 2011-09-08 | 2015-02-03 | George O. Mohler | Event forecasting system |
WO2013044141A2 (en) | 2011-09-22 | 2013-03-28 | Capgemini U.S. Llc | Process transformation and transitioning apparatuses, methods and systems |
WO2013052872A2 (en) | 2011-10-05 | 2013-04-11 | Mastercard International Incorporated | Nomination engine |
US8849776B2 (en) | 2011-10-17 | 2014-09-30 | Yahoo! Inc. | Method and system for resolving data inconsistency |
US8626545B2 (en) | 2011-10-17 | 2014-01-07 | CrowdFlower, Inc. | Predicting future performance of multiple workers on crowdsourcing tasks and selecting repeated crowdsourcing workers |
US8843421B2 (en) | 2011-11-01 | 2014-09-23 | Accenture Global Services Limited | Identification of entities likely to engage in a behavior |
US20130124193A1 (en) | 2011-11-15 | 2013-05-16 | Business Objects Software Limited | System and Method Implementing a Text Analysis Service |
US9159024B2 (en) * | 2011-12-07 | 2015-10-13 | Wal-Mart Stores, Inc. | Real-time predictive intelligence platform |
CN103167093A (en) | 2011-12-08 | 2013-06-19 | 青岛海信移动通信技术股份有限公司 | Filling method of mobile phone email address |
US8880420B2 (en) | 2011-12-27 | 2014-11-04 | Grubhub, Inc. | Utility for creating heatmaps for the study of competitive advantage in the restaurant marketplace |
US8843431B2 (en) | 2012-01-16 | 2014-09-23 | International Business Machines Corporation | Social network analysis for churn prediction |
US8909648B2 (en) | 2012-01-18 | 2014-12-09 | Technion Research & Development Foundation Limited | Methods and systems of supervised learning of semantic relatedness |
US9279898B2 (en) | 2012-02-09 | 2016-03-08 | Pgs Geophysical As | Methods and systems for correction of streamer-depth bias in marine seismic surveys |
US20130226944A1 (en) | 2012-02-24 | 2013-08-29 | Microsoft Corporation | Format independent data transformation |
GB2508573A (en) | 2012-02-28 | 2014-06-11 | Qatar Foundation | A computer-implemented method and computer program for detecting a set of inconsistent data records in a database including multiple records |
US8620963B2 (en) | 2012-03-08 | 2013-12-31 | eBizprise Inc. | Large-scale data processing system, method, and non-transitory tangible machine-readable medium thereof |
JP2013191187A (en) | 2012-03-15 | 2013-09-26 | Fujitsu Ltd | Processing device, program and processing system |
US20130263019A1 (en) | 2012-03-30 | 2013-10-03 | Maria G. Castellanos | Analyzing social media |
US20130262328A1 (en) | 2012-03-30 | 2013-10-03 | CSRSI, Inc. | System and method for automated data breach compliance |
US9298856B2 (en) | 2012-04-23 | 2016-03-29 | Sap Se | Interactive data exploration and visualization tool |
US8798354B1 (en) * | 2012-04-25 | 2014-08-05 | Intuit Inc. | Method and system for automatic correlation of check-based payments to customer accounts and/or invoices |
US9043710B2 (en) | 2012-04-26 | 2015-05-26 | Sap Se | Switch control in report generation |
US10304036B2 (en) | 2012-05-07 | 2019-05-28 | Nasdaq, Inc. | Social media profiling for one or more authors using one or more social media platforms |
EP2662782A1 (en) | 2012-05-10 | 2013-11-13 | Siemens Aktiengesellschaft | Method and system for storing data in a database |
US8788471B2 (en) * | 2012-05-30 | 2014-07-22 | International Business Machines Corporation | Matching transactions in multi-level records |
US9032531B1 (en) | 2012-06-28 | 2015-05-12 | Middlegate, Inc. | Identification breach detection |
US10163158B2 (en) | 2012-08-27 | 2018-12-25 | Yuh-Shen Song | Transactional monitoring system |
US20140068487A1 (en) | 2012-09-05 | 2014-03-06 | Roche Diagnostics Operations, Inc. | Computer Implemented Methods For Visualizing Correlations Between Blood Glucose Data And Events And Apparatuses Thereof |
WO2014052493A1 (en) * | 2012-09-25 | 2014-04-03 | Moneydesktop, Inc. | Aggregation source routing |
US20140095509A1 (en) | 2012-10-02 | 2014-04-03 | Banjo, Inc. | Method of tagging content lacking geotags with a location |
US9792004B2 (en) | 2012-10-08 | 2017-10-17 | Fisher-Rosemount Systems, Inc. | Derived and linked definitions with override |
US9104786B2 (en) | 2012-10-12 | 2015-08-11 | International Business Machines Corporation | Iterative refinement of cohorts using visual exploration and data analytics |
US8688573B1 (en) * | 2012-10-16 | 2014-04-01 | Intuit Inc. | Method and system for identifying a merchant payee associated with a cash transaction |
US8914886B2 (en) | 2012-10-29 | 2014-12-16 | Mcafee, Inc. | Dynamic quarantining for malware detection |
US9501761B2 (en) | 2012-11-05 | 2016-11-22 | Palantir Technologies, Inc. | System and method for sharing investigation results |
US9378030B2 (en) | 2013-10-01 | 2016-06-28 | Aetherpal, Inc. | Method and apparatus for interactive mobile device guidance |
US10504127B2 (en) | 2012-11-15 | 2019-12-10 | Home Depot Product Authority, Llc | System and method for classifying relevant competitors |
US20140143009A1 (en) | 2012-11-16 | 2014-05-22 | International Business Machines Corporation | Risk reward estimation for company-country pairs |
US20140157172A1 (en) | 2012-11-30 | 2014-06-05 | Drillmap | Geographic layout of petroleum drilling data and methods for processing data |
US20140156527A1 (en) | 2012-11-30 | 2014-06-05 | Bank Of America Corporation | Pre-payment authorization categorization |
US10672008B2 (en) | 2012-12-06 | 2020-06-02 | Jpmorgan Chase Bank, N.A. | System and method for data analytics |
US9497289B2 (en) | 2012-12-07 | 2016-11-15 | Genesys Telecommunications Laboratories, Inc. | System and method for social message classification based on influence |
US9294576B2 (en) | 2013-01-02 | 2016-03-22 | Microsoft Technology Licensing, Llc | Social media impact assessment |
US20140195515A1 (en) | 2013-01-10 | 2014-07-10 | I3 Analytics | Methods and systems for querying and displaying data using interactive three-dimensional representations |
US8639552B1 (en) | 2013-01-24 | 2014-01-28 | Broadvision, Inc. | Systems and methods for creating and sharing tasks |
US9892026B2 (en) | 2013-02-01 | 2018-02-13 | Ab Initio Technology Llc | Data records selection |
US20140222521A1 (en) | 2013-02-07 | 2014-08-07 | Ibms, Llc | Intelligent management and compliance verification in distributed work flow environments |
US20140222793A1 (en) | 2013-02-07 | 2014-08-07 | Parlance Corporation | System and Method for Automatically Importing, Refreshing, Maintaining, and Merging Contact Sets |
US9264393B2 (en) | 2013-02-13 | 2016-02-16 | International Business Machines Corporation | Mail server-based dynamic workflow management |
US8744890B1 (en) | 2013-02-14 | 2014-06-03 | Aktana, Inc. | System and method for managing system-level workflow strategy and individual workflow activity |
US20140244284A1 (en) | 2013-02-25 | 2014-08-28 | Complete Consent, Llc | Communication of medical claims |
US9286618B2 (en) * | 2013-03-08 | 2016-03-15 | Mastercard International Incorporated | Recognizing and combining redundant merchant designations in a transaction database |
US10140664B2 (en) | 2013-03-14 | 2018-11-27 | Palantir Technologies Inc. | Resolving similar entities from a transaction database |
US8855999B1 (en) | 2013-03-15 | 2014-10-07 | Palantir Technologies Inc. | Method and system for generating a parser and parsing complex data |
GB2513721A (en) | 2013-03-15 | 2014-11-05 | Palantir Technologies Inc | Computer-implemented systems and methods for comparing and associating objects |
GB2513720A (en) | 2013-03-15 | 2014-11-05 | Palantir Technologies Inc | Computer-implemented systems and methods for comparing and associating objects |
US8924388B2 (en) | 2013-03-15 | 2014-12-30 | Palantir Technologies Inc. | Computer-implemented systems and methods for comparing and associating objects |
US8903717B2 (en) | 2013-03-15 | 2014-12-02 | Palantir Technologies Inc. | Method and system for generating a parser and parsing complex data |
US9372929B2 (en) | 2013-03-20 | 2016-06-21 | Securboration, Inc. | Methods and systems for node and link identification |
US20140358789A1 (en) | 2013-05-30 | 2014-12-04 | B. Scott Boding | Acquirer facing fraud management system and method |
US9576248B2 (en) | 2013-06-01 | 2017-02-21 | Adam M. Hurwitz | Record linkage sharing using labeled comparison vectors and a machine learning domain classification trainer |
GB2517582A (en) | 2013-07-05 | 2015-02-25 | Palantir Technologies Inc | Data quality monitors |
US8601326B1 (en) | 2013-07-05 | 2013-12-03 | Palantir Technologies, Inc. | Data quality monitors |
US8838538B1 (en) | 2013-07-31 | 2014-09-16 | Palantir Technologies, Inc. | Techniques for replicating changes to access control lists on investigative analysis data |
US8938686B1 (en) | 2013-10-03 | 2015-01-20 | Palantir Technologies Inc. | Systems and methods for analyzing performance of an entity |
US8812960B1 (en) | 2013-10-07 | 2014-08-19 | Palantir Technologies Inc. | Cohort-based presentation of user interaction data |
US8832594B1 (en) | 2013-11-04 | 2014-09-09 | Palantir Technologies Inc. | Space-optimized display of multi-column tables with selective text truncation based on a combined text width |
US9356937B2 (en) | 2013-11-13 | 2016-05-31 | International Business Machines Corporation | Disambiguating conflicting content filter rules |
US10586234B2 (en) | 2013-11-13 | 2020-03-10 | Mastercard International Incorporated | System and method for detecting fraudulent network events |
US9105000B1 (en) | 2013-12-10 | 2015-08-11 | Palantir Technologies Inc. | Aggregating data from a plurality of data sources |
US20150161611A1 (en) | 2013-12-10 | 2015-06-11 | Sas Institute Inc. | Systems and Methods for Self-Similarity Measure |
US10356032B2 (en) | 2013-12-26 | 2019-07-16 | Palantir Technologies Inc. | System and method for detecting confidential information emails |
US8832832B1 (en) | 2014-01-03 | 2014-09-09 | Palantir Technologies Inc. | IP reputation |
US9129219B1 (en) | 2014-06-30 | 2015-09-08 | Palantir Technologies, Inc. | Crime risk forecasting |
US9256664B2 (en) | 2014-07-03 | 2016-02-09 | Palantir Technologies Inc. | System and method for news events detection and visualization |
US9483546B2 (en) | 2014-12-15 | 2016-11-01 | Palantir Technologies Inc. | System and method for associating related records to common entities across multiple lists |
-
2013
- 2013-03-14 US US13/827,491 patent/US10140664B2/en active Active
-
2014
- 2014-03-12 CA CA2845743A patent/CA2845743C/en not_active Expired - Fee Related
- 2014-03-13 AU AU2014201516A patent/AU2014201516A1/en not_active Abandoned
- 2014-03-13 GB GB1404499.4A patent/GB2513472A/en not_active Withdrawn
- 2014-03-14 NL NL2012438A patent/NL2012438B1/en not_active IP Right Cessation
- 2014-03-14 DE DE102014204827.3A patent/DE102014204827A1/en not_active Withdrawn
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20110099133A1 (en) * | 2009-10-28 | 2011-04-28 | Industrial Technology Research Institute | Systems and methods for capturing and managing collective social intelligence information |
CN102054015A (en) * | 2009-10-28 | 2011-05-11 | 财团法人工业技术研究院 | System and method of organizing community intelligent information by using organic matter data model |
US20130166480A1 (en) * | 2011-12-21 | 2013-06-27 | Telenav, Inc. | Navigation system with point of interest classification mechanism and method of operation thereof |
Cited By (53)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10061828B2 (en) | 2006-11-20 | 2018-08-28 | Palantir Technologies, Inc. | Cross-ontology multi-master replication |
US9501552B2 (en) | 2007-10-18 | 2016-11-22 | Palantir Technologies, Inc. | Resolving database entity information |
US9846731B2 (en) | 2007-10-18 | 2017-12-19 | Palantir Technologies, Inc. | Resolving database entity information |
US10733200B2 (en) | 2007-10-18 | 2020-08-04 | Palantir Technologies Inc. | Resolving database entity information |
US10747952B2 (en) | 2008-09-15 | 2020-08-18 | Palantir Technologies, Inc. | Automatic creation and server push of multiple distinct drafts |
US9348499B2 (en) | 2008-09-15 | 2016-05-24 | Palantir Technologies, Inc. | Sharing objects that rely on local resources with outside servers |
US11693877B2 (en) | 2011-03-31 | 2023-07-04 | Palantir Technologies Inc. | Cross-ontology multi-master replication |
US9880987B2 (en) | 2011-08-25 | 2018-01-30 | Palantir Technologies, Inc. | System and method for parameterizing documents for automatic workflow generation |
US10706220B2 (en) | 2011-08-25 | 2020-07-07 | Palantir Technologies, Inc. | System and method for parameterizing documents for automatic workflow generation |
US9715518B2 (en) | 2012-01-23 | 2017-07-25 | Palantir Technologies, Inc. | Cross-ACL multi-master replication |
US11182204B2 (en) | 2012-10-22 | 2021-11-23 | Palantir Technologies Inc. | System and method for batch evaluation programs |
US9898335B1 (en) | 2012-10-22 | 2018-02-20 | Palantir Technologies Inc. | System and method for batch evaluation programs |
US10140664B2 (en) | 2013-03-14 | 2018-11-27 | Palantir Technologies Inc. | Resolving similar entities from a transaction database |
US10977279B2 (en) | 2013-03-15 | 2021-04-13 | Palantir Technologies Inc. | Time-sensitive cube |
US9286373B2 (en) | 2013-03-15 | 2016-03-15 | Palantir Technologies Inc. | Computer-implemented systems and methods for comparing and associating objects |
US10152531B2 (en) | 2013-03-15 | 2018-12-11 | Palantir Technologies Inc. | Computer-implemented systems and methods for comparing and associating objects |
US10120857B2 (en) | 2013-03-15 | 2018-11-06 | Palantir Technologies Inc. | Method and system for generating a parser and parsing complex data |
US9495353B2 (en) | 2013-03-15 | 2016-11-15 | Palantir Technologies Inc. | Method and system for generating a parser and parsing complex data |
US10452678B2 (en) | 2013-03-15 | 2019-10-22 | Palantir Technologies Inc. | Filter chains for exploring large data sets |
US10762102B2 (en) | 2013-06-20 | 2020-09-01 | Palantir Technologies Inc. | System and method for incremental replication |
US10970261B2 (en) | 2013-07-05 | 2021-04-06 | Palantir Technologies Inc. | System and method for data quality monitors |
US9996229B2 (en) | 2013-10-03 | 2018-06-12 | Palantir Technologies Inc. | Systems and methods for analyzing performance of an entity |
US10198515B1 (en) | 2013-12-10 | 2019-02-05 | Palantir Technologies Inc. | System and method for aggregating data from a plurality of data sources |
US11138279B1 (en) | 2013-12-10 | 2021-10-05 | Palantir Technologies Inc. | System and method for aggregating data from a plurality of data sources |
US10579647B1 (en) | 2013-12-16 | 2020-03-03 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US10180977B2 (en) | 2014-03-18 | 2019-01-15 | Palantir Technologies Inc. | Determining and extracting changed data from a data source |
US10853454B2 (en) | 2014-03-21 | 2020-12-01 | Palantir Technologies Inc. | Provider portal |
US10242072B2 (en) | 2014-12-15 | 2019-03-26 | Palantir Technologies Inc. | System and method for associating related records to common entities across multiple lists |
US9483546B2 (en) | 2014-12-15 | 2016-11-01 | Palantir Technologies Inc. | System and method for associating related records to common entities across multiple lists |
US11302426B1 (en) | 2015-01-02 | 2022-04-12 | Palantir Technologies Inc. | Unified data interface and system |
US10103953B1 (en) | 2015-05-12 | 2018-10-16 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US10146853B2 (en) | 2015-05-15 | 2018-12-04 | International Business Machines Corporation | Determining entity relationship when entities contain other entities |
US12056718B2 (en) | 2015-06-16 | 2024-08-06 | Palantir Technologies Inc. | Fraud lead detection system for efficiently processing database-stored data and automatically generating natural language explanatory information of system results for display in interactive user interfaces |
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CA2845743C (en) | 2020-03-31 |
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GB201404499D0 (en) | 2014-04-30 |
AU2014201516A1 (en) | 2014-10-02 |
CA2845743A1 (en) | 2014-09-14 |
DE102014204827A1 (en) | 2014-09-18 |
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NL2012438A (en) | 2014-09-16 |
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